Dynamic generation of guided pages

ABSTRACT

Configuring guided pages in this document may include preparing a columnar-based multi-domain business intelligence data set, from a plurality of sources of data that can be independently formatted, by processing the data from the plurality of sources with a data calculation engine that organizing the columns to align with user specified and/or automatically determined dimensions that are associated with a business and populating the columns with data that relates to each of the user specified dimensions from each of the plurality of sources of data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/168,339 filed May 29, 2015. This application is acontinuation in part of U.S. patent application Ser. No. 14/954,795,filed Nov. 30, 2015, which is a continuation of U.S. patent applicationSer. No. 13/907,274, filed May 31, 2013, which claims the benefit ofU.S. Provisional Patent Application No. 61/655,847 filed on Jun. 5, 2012and U.S. Provisional Patent Application No. 61/789,527 filed on Mar. 15,2013. Each of the foregoing applications is incorporated herein byreference in its entirety.

BACKGROUND

Field

The present invention is related to tablet-based business intelligencemethods and systems.

Description of the Related Art

Business intelligence systems require custom programming and maintenanceby professional systems analysts, leaving the executive or employee withlittle direct control over the means of data discovery. Dynamic linkingof data is on-line business intelligence systems tends to be limited todiving vertically within a data domain, thereby preventing a user forreadily viewing related data in other data domains without a cumbersomeregeneration process. Also tablet-based business intelligence systemsare in high demand and no business intelligence solutions exist toeffectively utilize a tablet-based device.

SUMMARY

Throughout this disclosure wine and spirits (liquor) industries are usedas examples of the methods and systems described herein. The use of wineand spirits is merely exemplary and in no way is meant to limit theinventive concepts, applications, markets, or uses of the methods andsystems described herein. Many other industries, such as automotiveparts, healthcare, financial, and nearly any other business concern cantake advantage of and gain significant benefit from the methods andsystems related to guided page navigation, data discovery, distinct datadomain navigation, dynamic presentation path selection, times seriesmodeling, synchronizing for off-line use, and the like that aredescribed herein.

The methods and systems for configuring guided pages in this documentmay include preparing a columnar-based multi-domain businessintelligence data set, from a plurality of sources of data that can beindependently formatted, by processing the data from the plurality ofsources with a data calculation engine that organizes the columns toalign with user specified and/or automatically determined dimensionsthat are associated with a business and populating the columns with datathat relates to each of the dimensions from each of the plurality ofsources of data.

The methods and systems for configuring guided pages in this documentmay include generating a plurality of guided navigation pages inresponse to a user inquiry to provide guided navigation pages to satisfydata viewing requirements of a workflow of a business, by processingdata with a processor that accelerates page generation operation throughutilization of native machine memory caching of similar c-based data.The user inquiry includes logical data access instructions [queries]that are converted into machine-specific code to further improvecomputer performance. Machine execution of a user query is optimized togroup query functions into common execution threads based on usage ofcommon data sets by different functions, and to group commoncalculations across query functions. These methods and systems mayfurther comprise stateless query execution and/or stateless queryexecution that accesses columnar data set data in a native machine cachememory. These methods and systems may further comprise a visual dataintegration user interface adapted to facilitate automaticallysuggesting a guided navigation page data query function.

The methods and systems for configuring guided pages in this documentmay include synching a client with a server to convert data setfiltering to dataset access.

The methods and systems for configuring guided pages in this documentmay include presenting domain-specific data from one of a first data setcomprising data associated with a plurality of data domains and a seconddata set that is specific to the presented domain in response to a userindication of the presented domain and based on a sync status of thefirst data set.

The methods and systems for configuring guided pages in this documentmay include presenting parameter-specific data as either a filteredoutput of a parameter-agnostic data set or as an unfiltered output of aparameter-specific data set based on a status of synching of theparameter-agnostic data set with a server

The methods and systems for configuring guided pages in this documentmay include generating a plurality of data sets comprising amulti-domain data set and at least one domain-specific subset of themulti-domain data set in response to a synch request from a clientdevice, wherein the synch request indicates the at least one domain.

The methods and systems for configuring guided pages in this documentmay include limiting access to domain-specific data from a multi-domaindata set by a user of a client device by one of configuring adomain-specific filter for the multi-domain data set and accessing adomain-specific data set based on synch status of the multi-domain dataset.

The methods and systems for configuring guided pages in this documentmay include a guided navigation page in which aggregated data for aplurality of data domains of a multi-domain data set is presented in afirst displayable tab and domain-specific data for a user-identifieddomain of the plurality of data domains is presented in a seconddisplayable tab, wherein the second tab represents a filtered view ofthe data in the first tab. Updates to the data presented in the firsttab that impact the filtered view result in corresponding changes to thesecond tab.

The methods and systems for configuring guided pages in this documentmay include one-click dim-count expansion, that may be performed withoutrecalculating dimension element counts, and or may be independent ofaccess to dimension source data element counts.

The methods and system for configuring guided pages in this documentinclude methods and systems that may be used for configuring a set ofguided pages for operation of a business activity based on a workflowfor the activity, and preconfigured industry-specific knowledge fromsource data that may be relevant to the business activity using a datacalculation engine that organizes a columnar-based multi-domain data setso that the columns align with user specified and/or automaticallydetermined dimensions that are associated with a business and populatingthe columns with data that relates to each of the dimensions from eachsource of data. In addition, each page of the set of guided pages may beorganized around one or more industry-specific data dimensions. Inaddition, at least one of the one or more industry-specific datadimensions may be a core dimension. At least one of the one or moreindustry-specific data dimensions may be a dynamic dimension. At leastone page of the set of guided pages may be a presentation object. Thepresentation object may be a portion of a document.

The methods and systems for configuring a set of guided documents mayinclude methods and systems that may be capable of configuring a set ofguided documents for operation of the business activity based on theworkflow for the activity, and preconfigured industry-specificknowledge, from source data that may be relevant to the businessactivity using a data calculation engine that organizes a columnar-basedmulti-domain data set so that the columns align with user specifiedand/or automatically determined dimensions that are associated with abusiness and populating the columns with data that relates to each ofthe dimensions from each source of data. Configuring a set of guideddocuments may include configuring a set of points of entry to access theguided documents and the point of entry may be organized around one ormore industry-specific data dimensions. The at least one of the one ormore industry-specific data dimensions may be a core dimension. The atleast one of the one or more industry-specific data dimensions may be adynamic dimension.

The methods and systems for navigation of guided pages disclosed hereinmay include methods and systems that may be configured for navigationamong a set of guided pages for operation of a business activity. Thenavigation may be based on a workflow for the activity,industry-specific knowledge, and at least one core data dimension of thebusiness. The navigation may allow direct movement between guided pagesin distinct data domains. The methods and systems for navigation ofguided pages disclosed herein may also include a guided page navigationengine that may be configured to interact with the sources of data,existing guided pages, the workflow, and at least one core datadimension to generate a next guided page for the user. The system may beconfigured so as to allow a direct movement between distinct data domainsimply by selecting a selectable items in a first guided page thatincludes data from a first distinct data domain that shares a commoncore dimension with data from a second distinct data domain.

The methods and systems for preconfiguring page to page navigationdisclosed herein may include methods and systems for configuring anavigation map for navigation among a set of guided pages for operationof a business activity allowing industry-specific knowledge-based guideddiscovery of data based on a modeling method. The modeling method maycomprise multi-source data collection, data calculation with ananalytics engine that organizes a columnar-based multi-domain data setso that the columns align with user specified and/or automaticallydetermined dimensions that are associated with a business and populatingthe columns with data that relates to each of a plurality of dimensionsfrom each source of data, and industry-specific core data dimensiondiscovery. The modeling method may be further configured to useindustry-specific business rules to facilitate the rapid access. Themulti-source data collection may include integrating data from aplurality of disparate data sources.

The methods and systems of guided data page generation may include apage generation engine that may be configured for presenting a set ofbusiness activity guided pages of data from multiple disparate datasources. The pages may include actionable elements and points of entrythat may be determined based on an industry-specific knowledge-baseddirected navigation map and that may activate pathways to areas ofinterest based on a workflow for the business activity.

The methods and systems of guided content page generation may include acontent page generation engine that may be configured for presenting aset of business activity guided pages of content based on sources ofdata associated with the business activity. The pages may includeactionable elements and points of entry that may be determined based onan industry knowledge directed navigation map and that may activatepathways to areas of interest based on a workflow for the businessactivity.

The methods and systems of guided navigation disclosed herein mayinclude methods and systems for guided navigation throughout and acrossdistinct data domains that may be clustered around industry-specificcore dimensions of source data. The industry-specific core dimensionsmay be determined during a data discovery process. The method of guidednavigation may be encoded into the guided page navigation engine.

The methods and systems for generating guided pages of industry-specificactionable elements may include methods and systems of guided navigationthroughout and across distinct data domains via a set of guided pagesfor operation of a business activity. The pages may include row andcolumn headings that may represent data associated with at least one ofindustry-specific data dimensions and domains related to a core datadimension. Each row heading, each column heading, and each intersectingcell may be a point of entry to a guided page that comprises at leastone of data and other content related to the row and/or columnsassociated with the point of entry. The industry-specific data dimensionmay be a core data dimension. The row and column headings may representdata associated with the same data dimension. The row and columnheadings may represent data associated with the same data domain. Thecolumn headings may represent data associated with an industry-specificdata. The dimension and row headings may represent data associated witha data domain. The column headings may represent data associated with adata domain and the row headings may represent data associated with anindustry-specific data dimension.

The methods and systems for facilitating directly jumping to relevantdata/dimensions in a tabular user interface may include a user interfacethat may be configured for displaying tabular data. The user interfacemay enable a user to select a data item in the table. The user may thenbe presented with a new data domain and set of dimensions that may havebeen pre-determined by an analyst and found to be likely to be relevantto the user. The relevancy may be based on the business context of theuser and prior navigation steps of the user within the interface. Aguided page navigation engine associated with the interface may beconfigured to store a navigation history of a user.

The methods and systems for facilitating directly jumping to relevantdata/dimensions in a graph-based user interface may include a userinterface that may be configured for displaying graph-based data. Theuser interface may enable a user to select an item in a graph. The usermay then be presented with a new data domain and set of dimensions thatmay have been pre-determined by an analyst and found to be likely to berelevant to the user. The relevancy may be based on the business contextof the user and prior navigation steps of the user within the interface.A guided page navigation engine associated with the interface may beconfigured to store a navigation history of a user.

The methods and systems for facilitating directly jumping to relevantdata/dimensions in a content presentation user interface may include auser interface that may be configured for presenting image ordocument-type content. The user interface may enable a user to select anactionable item, such as a point of entry in the user interface. Theuser may then be presented with document-type content from a new datadomain and set of dimensions that may have been pre-determined by ananalyst and found to be likely to be relevant to the user. The relevancymay be based on the business context of the user and prior navigationsteps of the user within the interface. A guided page navigation engineassociated with the interface may be configured to store a navigationhistory of a user.

The methods and systems for facilitating directly jumping to relevantdata/dimensions in a tabular user interface may include a user interfacethat may be configured for displaying tabular data. The user interfacemay enable a user to select a data item in the table. The user may thenbe presented with a new data domain and set of dimensions that may havebeen pre-determined by an analyst and found to be likely to be relevantto the user. The relevancy may be based on the business context of theuser and prior navigation steps of the user within the interface. Thedimensions of the receiving page may be determined based on the dataselected by a user in the interface and the data on the receiving pagemay be filtered based on prior actions of the user in the userinterface.

Alternatively, the user interface may be configured to display a graphthat may be presented with associated dimensions and actionable itemsfor generating a receiving page. As described above, the dimensions ofthe receiving page may be determined based on an item selected by a userin the interface and the data on the receiving page may be filteredbased on prior actions of the user in the user interface.

Alternatively, the user interface may be configured to display contentthat is associated with at least one core dimension and actionable itemsfor generating a receiving page. The dimensions of the receiving pagemay be determined based on an item selected by a user in the interfaceand the data on the receiving page may be filtered based on prioractions of the user in the user interface.

The methods and systems of creating a large number of reports for a dataset may include creating reports that may represent a wide range ofpossible views of the data set based on dimensions of data that may bepresent or available in the data set that may comprise a range of datasources that may be used by a business including internal and externaldata sources. The reports may have tables that have dimensions that maybe calculated or dynamic, and values or summaries that may correspond tothose dimensions. The method may further include relating values to thedimensions to which they relate on the screen as presented in aparticular report as well as to many other dimensions in the collectionof reports that are not shown. The method may further include enabling auser to select a dimension or a value. In addition, selecting apresented dimension may give the user the chance to see a report keyedto the presented dimension. The report may include values and possiblyother dimensions that may be associated with the selected dimension. Inaddition, selecting a value may allow a user to see other dimensionsthat may have a relationship with the selected value along with othervalues that may be associated with the other dimensions. The method mayfurther include configuring the pre-arranged, guided navigation so thata set of new dimensions and values that may be selected may be shownbased on a prescribed flow.

The methods and systems of creating a large number of reports in graphform for a data set may include creating reports that may represent awide range of possible views of the data set based on dimensions of datathat may be present or available in the data set that may comprise arange of data sources that may be used by a business including internaland external data sources. The reports may include graphs that havedimensions that may be calculated or dynamic, and values or summariesthat may correspond to those dimensions. The method may include relatingvalues to the dimensions to which they may be graphed in a particularreport as well as to many other dimensions in the collection of reports.The method may further include enabling a user to select a dimension ora graphed value. In addition, selecting a presented dimension or graphedvalue may give the user the chance to see a report keyed to theselection. The report may include graphs and possibly other dimensionsthat may be associated with the selected dimension. In addition,selecting a graphed value may allow a user to see other dimensions thatmay have a relationship with the selection along with other graphedvalues that may be associated with the other dimensions. The method mayfurther include configuring the pre-arranged, guided navigation so thata set of new dimensions and graphed values may be selected and may beshown based on a prescribed flow.

The methods and systems may include configuring a large number ofcontent items associated with a data set for viewing in a tablet userinterface. The content items may be associated with a wide range ofpossible views of the data set based on dimensions of data that may bepresent or available in the data set that may comprises a range of datasources used by a business including internal and external data sources.The content items may be further associated with values or summariesthat may correspond to the dimensions, which may be calculated ordynamic. The method may be configured so that the content items may bepresented with a portion of the associated dimensions or values. Inaddition, content items may be associated with many other dimensions.The method may be further configured so that a user may select a contentitem in the user interface to see other content items that may haveassociated dimensions or values associated with the selection. Themethod may further include configuring the pre-arranged, guidednavigation so that may selects data and show data based on a prescribedflow. This interface may be programmed so that the next page may bedetermined based on what is believed to be most usable by the user,taking into account a business case/industry context and user historicalviews. In addition, the interface methods as mentioned above may beprogrammed so that the flow may be iterative, such that a user mayexplore many values and dimensions by moving from one value to anothervalue and from one dimension to another dimension.

The methods and systems for generating guided pages from disparate datasources disclosed herein may include a method of generating a set ofguided pages for operation of a business activity comprising gatheringdata from a wide range of data sources into a data repository;integrating data with a script-based domain editor. A script for thescript-based domain editor may be generated based on use of a visualintegration facility. The method may further include determiningdimensions of the data, including determining dynamic dimensions. Themethod further includes determining associations of data withdimensions. The method also includes calculating one or more of datavalues and summaries for the dimensions and organizing the data into acolumnar-based multi-domain data set so that the columns align with userspecified and/or automatically determined dimensions that are associatedwith a business, wherein the columns are populated with data thatrelates to each of a plurality of dimensions from each source of data.The method further includes configuring a set of business rules foraccessing the columnar-data set. The method includes generating aplurality of linkable pages that may be suitable for presentation on atablet computing device. The method includes determining permissiblelinks among the plurality of linkable pages and associating particularlinks with actionable points of entry in the guided pages so that thelinkages may suit a workflow for a particular business activity. Themethod may be configured so that determining associations of data withdimensions may include determining associations of data with coredimensions.

In addition, the linkable pages may be configured to present data anddimensions in tabular or graphical formats. The linkable pages maypresent document image content associated with the workflow.

The method may be configured for further including calculating datasummaries to facilitate guided page generation. In addition, the datasummary may be based on a dynamic dimension.

The methods and systems of generating and navigating among guided pagesmay include associating documents to at least one core dimension and todata clustered around the core dimension so that the documents may beviewed in a tablet-based user interface in response to a user selectionof an actionable item in the interface.

The methods and systems described herein may include summarizing datainto time period buckets for each of a plurality of core data dimensionsthat may be determined in a discovery and modeling process of data in adata set so that a page generation engine serves time summarized pagesfor at least one of the core data dimensions without requiring access tothe data set. The guided page navigation engine may be operably coupledto a cache of guided pages that may keep a record of all time summariesand may assist in formation of next guided page based on previous guidedpages.

The methods and systems for times series summary modeling may includegenerating a columnar-based multi-domain data set of time-period basedsummaries of data of core data dimensions that are determined in adiscovery process of data in a data set. In addition, the time-periodbased summaries may be generated for each of a plurality of coredimensions. The time period based summaries may be stored in a cache ofguided pages or at a location elsewhere from where the data stored astime period based summaries may be accessed by the page generationengine with a need for accessing the plurality of core dimensions frommain data set. This may facilitate in securing access to core dimensiondata and also increase efficiency of the user as the user may now browseprevious data without a need to be connected to the core dimension data.

The methods of time series summary modeling may alternatively includegenerating a columnar-based multi-domain data set of time-period baseddata summaries for operation of a business activity comprising gatheringdata from a wide range of data sources into a data repository. Themethod may be further including integrating data with a script-basedintegrator. The script for the script-based integrator may be generatedbased on use of a visual integration facility. The method may furtherinclude determining dimensions of the data, including determiningdynamic dimensions. The method may further include determiningassociations of data with dimensions. In addition, the method may alsoinclude calculating one or more of data values and summaries for thedimensions, including time-period based summaries and organizing thedata into columns that align with user specified and/or automaticallydetermined dimensions that are associated with a business, the columnsbeing populated with data that relates to each of a plurality ofdimensions from each source of data. The method may further includeconfiguring a set of business rules for accessing the columnar-baseddata set.

In addition, the method above may further include generating a pluralityof linkable pages that may be suitable for presentation on a tabletcomputing device. The method may be further include determiningpermissible links among the plurality of linkable pages and associatingparticular links with actionable points of entry in the guided pages sothat the linkages may suit a workflow for a particular businessactivity.

The methods and systems described herein may include processing datafrom distinct data domains to facilitate determining at least one coredimension that is common to a portion of the distinct data domains thatare captured in the columnar-based multi-domain data set and linking thecolumnar-based multi-domain data set in a guided page navigation mapthat enables navigation within and among a plurality of distinct datadomains via the common core dimension.

The methods and systems described herein may include presenting in aguided page user interface data associated with a core dimension of afirst data source in response to a [user] selection in the guided pageuser interface of an item [of data] from a second data source, whereinthe first and second data sources represent different data domains andwherein the item of data is associated with the core dimension.

The methods and systems described herein may include a guided page userinterface that enables presenting items of data from a first datadomain, and in response to a user selection of a presented item of data,presenting items of data from a second data domain based on the firstand second data domains having a common core dimension. In these methodsthe first and second data domains are distinct domains. Alternatively,the selected item of data is associated with the common core dimension.In addition, the common core dimension is determined during datadiscovery of a plurality of data sources representing the first andsecond data domains. In yet another alternative, the user interface isadapted to access at least two distinct data sources representing thefirst data domain and the second data domain.

The methods and systems described herein may include automatedidentification of navigation among pages across distinct source datasets and data domains that share a common core dimension and based onhistorical user actions.

The methods and systems described herein may include dynamicallynavigating a presentation that comprises a set of guided pages ofcontent wherein groups of presentation pages are linked to other groupsof presentation pages through association with a set of core dimensionsthat are determined during a data discovery process and that are commonto the presentation pages.

The methods and systems relating to guided navigation throughout andacross data domains that are clustered around industry-specific coredimensions of data that are determined during a data discovery process.

In one aspect of the methods and systems described herein, a systemincludes a data set may include data from a plurality of data domains,the data set characterized by data having a plurality ofindustry-specific core dimensions, wherein at least one of the pluralityof industry-specific core dimensions may be common to at least two ofthe plurality of data domains; a plurality of guided pages thatfacilitate data discovery through user interactions with at least one ofa displayable actionable element and an actionable point of entryassociated with at least one of the industry-specific core dimensions,wherein user interaction with at least one of a displayable actionableelement and an actionable point of entry results in presenting at leastone of: a guided page of a current data domain; and a guided page of analternate data domain that may be associated with an industry-specificcore dimension that may be common to the current and alternate datadomains; guided page high level description information that facilitatesnavigation among the plurality of guided pages; and guided page datastructure information that facilitates orienting information derivedfrom the data in the data set in an interactive electronic display.

The displayable actionable elements are determined based on an industryknowledge directed navigation map. The displayable actionable elementsactivate pathways to areas of interest based on a workflow for abusiness activity associated with the data set. The points of entry aredetermined based on an industry knowledge directed navigation map. Thepoints of entry activate pathways to areas of interest based on aworkflow for the business activity associated with the data set. Thesystem facilitates data discovery based on a guided page navigation mapthat enables navigation within and among the plurality of data domainsvia the common core dimension. The data from any of the plurality ofdata domains that have a common core dimension can be accessed via anactionable element of a guided page that may include data of any of theplurality of data domains that have the common core dimension. In orderto facilitate data discovery, data associated with a core dimension of afirst data domain may be presented in a guided page user interface inresponse to a selection in the guided page user interface of anactionable element associated with a second data domain. The first andsecond data domains represent different data domains and wherein theactionable element may be also associated with the core dimension. Theactionable element depicts an item of data associated with the coredimension. The actionable element depicts an item of data associatedwith the second data domain. In an example, a guided page that may bepresented in response to user interaction with one of the displayableactionable elements or points of entry may be based on a workflow for abusiness activity associated with the data set. The plurality of guidedpages may include groups of diverse-content pages of a businesspresentation, navigation among the groups being based on a coredimension that may be common to the groups. In an example, the pages ina group are independent of any specific data domain. In an example, thedata set may include data from a plurality of data domains derived froma plurality of data sources including customer data sources associatedwith operating a business and external data sources. The customer datasources may include at least two sources selected from the listconsisting of flat files, spreadsheets, data warehouses, SQL databases,non-standard formatted data, legacy systems, transactional systems, andEnterprise Resource Planning (ERP) systems. The external data sourcesmay include at least two sources selected from the list consisting ofthird party feeds, market data, end-user data, state data, county data,regional data, and demo data. In an example, the plurality ofindustry-specific core dimensions may be predetermined by anindustry-specific expert. In an example, a result of user interactionwith one of the displayable actionable elements or points of entry maybe predetermined by an industry-specific expert. Alternatively, a resultof user interaction with one of the displayable actionable elements orpoints of entry may be determined by a combination of factors selectedfrom the set consisting of: input from an industry-specific expert, arole of the user, a workflow of the user, previously presented guidedpages, a core dimension associated with a selected actionable element orpoint of entry; business rules that facilitate configuring the pluralityof data domains, candidate next domains, and metadata associated atleast one of a presented guided page and a selected actionable elementor a point of entry. In an example, the data set may include calculatedsummaries of data suitable for presentation in a guided page based onthe guided page data structure information and non-summarized data thatmay be processed to comply with the data structure information justprior to being presented in a guided page. Alternatively, the data setmay include dimensions, summary fields, and information fields.Otherwise, the data set may include summaries of all data associatedwith all common core dimensions. Alternatively, the data set may includea plurality of core dimensions and a plurality of dynamic dimensions.For example, to present a guided page based on one of the plurality ofdynamic dimensions may include processing that data to conform with theguided page data structure information in response to a user interactionwith one of the displayable actionable elements or points of entry thatmay be not required for presenting a guided page based on one of theplurality of core dimensions. In an example, the data set may include atleast one central dimension. The central dimension may include a logicalgrouping of data in the data set around which core dimensions, dynamicdimensions, and data domains can be clustered. Alternatively, thecentral dimension may include a logical grouping of data that representsa subset of data associated with a core dimension. In an example, thedata set may include data associated with a variety of accesspermissions that are automatically granted based on relevancy of thedata with at least one of the core dimensions of the data set. Thepermissions may be automatically granted based on a combination of userrole, business workflow, and industry requirements configured by anindustry expert. In an example, a guided page may include a tabular viewof data items. For example, the guided page may include points of entryas column and row headers of a matrix of data items. Further, data itemsmay include actionable elements. Moreover, a portion of data items mayinclude actionable elements. In an example, a guided page may include achart with chart elements that represent data associated with a datadomain, such that, a portion of chart elements may include actionableelements. In an example, a guided page may include one of an accesspage, a central page, an overview page, and a list page. The access pagemay be associated with a core dimension. The central page may beassociated with a central dimension. The central dimension may include alogical grouping of data in the data set around which core dimensions,dynamic dimensions, and data domains can be clustered. Alternatively,the central dimension may include a logical grouping of data thatrepresents a subset of data associated with a core dimension. Theoverview page may be associated with a plurality of dimensions. In anexample, a user selection of an actionable element or point of entry onan access page results in presenting a guided page selected from the setconsisting of access pages, central pages, and overview pages.Alternatively, a user selection of an actionable element or point ofentry on a central page results in presenting a guided page selectedfrom the set consisting of central pages, access pages, and list pages.Further, a user selection of an actionable element or point of entry ona list page may result in presenting a guided page selected from the setconsisting of central pages, overview pages, and list pages. Moreover, auser selection of an actionable element or point of entry on an overviewpage may result in presenting a guided page selected from the setconsisting of overview pages, access pages, and list pages.

In another aspect of the methods and systems described herein, a methodincludes using a processor to access a data set may include data from aplurality of data domains and a plurality of industry-specific coredimensions, wherein at least one of the plurality of industry-specificcore dimensions may be common to at least two of the plurality of datadomains; facilitating data discovery through user interactions withdisplayable actionable elements, associated with the industry-specificcore dimensions, that are presented by the processor in a plurality ofguided pages on an interactive electronic display, wherein userinteraction with one of the displayable actionable elements results inpresenting at least one of: a guided page of a current data domain; anda guided page of an alternate data domain that may be associated with anindustry-specific core dimension that may be common to the current andalternate data domains; facilitating navigation among the plurality ofguided pages based on guided page high level description informationaccessible by the processor; and orienting information derived from thedata in the data set in an interactive electronic display based onguided page data structure information that may be accessible by theprocessor.

The displayable actionable elements may be determined based on anindustry knowledge directed navigation map. In an example, thedisplayable actionable elements activate pathways to areas of interestbased on a workflow for a business activity associated with the dataset. In an example, facilitating data discovery may be based on a guidedpage navigation map that enables navigation within and among theplurality of data domains via the common core dimension. In an example,data from any of the plurality of data domains that have a common coredimension can be accessed via an actionable element of a guided pagethat may include data of any of the plurality of data domains that havethe common core dimension. In an example, facilitating data discoverymay include presenting in a guided page user interface data associatedwith a core dimension of a first data domain in response to a selectionin the guided page user interface of an actionable element associatedwith a second data domain, wherein the first and second data domainsrepresent different data domains and wherein the actionable element maybe also associated with the core dimension. The actionable elementdepicts an item of data associated with the core dimension.Alternatively, the actionable element depicts an item of data associatedwith the second data domain. In an example, a guided page that may bepresented in response to user interaction with one of the displayableactionable elements may be based on a workflow for a business activityassociated with the data set. In another example, the plurality ofguided pages may include groups of diverse-content pages of a businesspresentation, navigation among the groups being based on a coredimension that may be common to the groups. The pages in a group may beindependent of any specific data domain. In an example, the data set mayinclude data from a plurality of data domains derived from a pluralityof data sources including customer data sources associated withoperating a business and external data sources. The customer datasources may include at least two sources selected from the listconsisting of flat files, spreadsheets, data warehouses, SQL databases,non-standard formatted data, legacy systems, transactional systems, andEnterprise Resource Planning (ERP) systems. The external data sourcesmay include at least two sources selected from the list consisting ofthird party feeds, market data, end-user data, state data, county data,regional data, and demo data. In an example, the plurality ofindustry-specific core dimensions may be predetermined by anindustry-specific expert. In an example, a result of user interactionwith one of the displayable actionable elements or points of entry maybe predetermined by an industry-specific expert. Alternatively, a resultof user interaction with one of the displayable actionable elements orpoints of entry may be determined by a combination of factors selectedfrom the set consisting of: input from an industry-specific expert, arole of the user, a workflow of the user, previously presented guidedpages, a core dimension associated with a selected actionable element orpoint of entry; business rules that facilitate configuring the pluralityof data domains, candidate next domains, and metadata associated atleast one of a presented guided page and a selected actionable elementor a point of entry. In an example, the data set may include calculatedsummaries of data suitable for presentation in a guided page based onthe guided page data structure information and non-summarized dataprocessed to comply with the data structure information just prior tobeing presented in a guided page. Alternatively, the data set mayinclude dimensions, summary fields, and information fields. Further, thedata set may include summaries of all data associated with all commoncore dimensions. Moreover, the data set may include a plurality of coredimensions and a plurality of dynamic dimensions. A guided page based onone of the plurality of dynamic dimensions may include processing thatdata to conform with the guided page data structure information inresponse to a user interaction with one of the displayable actionableelements or points of entry that may be not required for presenting aguided page based on one of the plurality of core dimensions. In anexample, the data set may include at least one central dimension. Acentral dimension may include a logical grouping of data in the data setaround which core dimensions, dynamic dimensions, and data domains canbe clustered. Alternatively, a central dimension may include a logicalgrouping of data that represents a subset of data associated with a coredimension. In an example, the data set may include data associated witha variety of access permissions that are automatically granted based onrelevancy of the data with at least one of the core dimensions of thedata set. The permissions are automatically granted based on acombination of user role, business workflow, and industry requirementsconfigured by an industry expert. In an example, a guided page mayinclude a tabular view of data items. The guided page may include pointsof entry as column and row headers of a matrix of data items. Further,data items may include actionable elements. Moreover, a portion of dataitems may include actionable elements. In another example, a guided pagemay include a chart with chart elements that represent data associatedwith a data domain, such that, a portion of chart elements may includeactionable elements. Further, a guided page may include one of an accesspage, a central page, an overview page, and a list page. The access pagemay be associated with a core dimension. The central page may beassociated with a central dimension. The central dimension may include alogical grouping of data in the data set around which core dimensions,dynamic dimensions, and data domains can be clustered. Alternatively, acentral dimension may include a logical grouping of data that representsa subset of data associated with a core dimension. The overview page maybe associated with a plurality of dimensions. In an example, a userselection of an actionable element or point of entry on an access pageresults in presenting a guided page selected from the set consisting ofaccess pages, central pages, and overview pages. Alternatively, a userselection of an actionable element or point of entry on a central pagemay result in presenting a guided page selected from the set consistingof central pages, access pages, and list pages. Further, a userselection of an actionable element or point of entry on a list page mayresult in presenting a guided page selected from the set consisting ofcentral pages, overview pages, and list pages. Moreover, a userselection of an actionable element or point of entry on an overview pageresults in presenting a guided page selected from the set consisting ofoverview pages, access pages, and list pages.

The methods and systems relating to navigation among a set of guidedpages for operation of a business activity. The navigation may be basedon a workflow for the activity, and at least one core data dimension ofthe business. The navigation may allow direct movement between guidedpages in different domains.

In an aspect of the methods and systems described herein, a system mayinclude a data set may include data from a plurality of data domainsderived from a plurality of industry-specific data sources, the data setcharacterized by data having a plurality of core dimensions relating toparameters of a business, wherein at least one of the plurality of coredimensions may be common to at least two of the plurality of datadomains; a plurality of guided pages that facilitate direct navigationamong the plurality of data domains in response to user interactionswith displayable actionable elements presented in a first guided pageassociated with a workflow of a business activity of the business,wherein user interaction with one of the displayable actionable elementsin the first guided page results in presenting a second guided page of adata domain associated with the common core dimension other than thedata domain associated with the first guided page, and wherein theplurality of guided pages comprise information determined by an industryexpert to be pertinent to operation of the business activity; guidedpage high level description information that facilitates navigationamong the plurality of guided pages; and guided page data structureinformation that facilitates orienting information derived from the datain the data set in an interactive electronic display.

The displayable actionable elements may be determined based on anindustry knowledge directed navigation map. The displayable actionableelements may activate pathways to areas of interest based on a workflowfor a business activity associated with the data set. In an example,data from any of the plurality of data domains that have a common coredimension may be accessed via an actionable element of a guided pagethat may include data of any of the plurality of data domains that havethe common core dimension. In an example, the plurality of guided pagesmay include groups of diverse-content pages of a business presentation,navigation among the groups being based on a core dimension that may becommon to the groups. The pages in a group may be independent of anyspecific data domain. In an example, the data set may include data froma plurality of data domains derived from a plurality of data sourcesincluding customer data sources associated with operating a business andexternal data sources. The customer data sources comprise at least twosources selected from the list consisting of flat files, spreadsheets,data warehouses, SQL databases, non-standard formatted data, legacysystems, transactional systems, and Enterprise Resource Planning (ERP)systems. The external data sources may include at least two sourcesselected from the list consisting of third party feeds, market data,end-user data, state data, county data, regional data, and demo data. Inan example, the plurality of industry-specific core dimensions may bepredetermined by an industry-specific expert. In an example, a result ofuser interaction with one of the displayable actionable elements may bepredetermined by an industry-specific expert. Alternatively, a result ofuser interaction with one of the displayable actionable elements may bedetermined by a combination of factors selected from the set consistingof: input from an industry-specific expert, a role of the user, aworkflow of the user, previously presented guided pages, a coredimension associated with a selected actionable element or point ofentry; business rules that facilitate configuring the plurality of datadomains, candidate next domains, and metadata associated at least one ofa presented guided page and a selected actionable element or a point ofentry. In an example, the data set may include calculated summaries ofdata suitable for presentation in a guided page based on the guided pagedata structure information and non-summarized data that may be processedto comply with the data structure information just prior to beingpresented in a guided page. Alternatively, the data set may includedimensions, summary fields, and information fields. Further, the dataset may include summaries of all data associated with all common coredimensions. Moreover, the data set may include a plurality of coredimensions and a plurality of dynamic dimensions. In an example, aguided page may be presented based on one of the plurality of dynamicdimensions may include processing that data to conform with the guidedpage data structure information in response to a user interaction withone of the displayable actionable elements that may be not required forpresenting a guided page based on one of the plurality of coredimensions. In an example, the data set may include at least one centraldimension. A central dimension may include a logical grouping of data inthe data set around which core dimensions, dynamic dimensions, and datadomains can be clustered. Alternatively, a central dimension may includea logical grouping of data that represents a subset of data associatedwith a core dimension. In an example, the data set may include dataassociated with a variety of access permissions that are automaticallygranted based on relevancy of the data with at least one of the coredimensions of the data set. The permissions are automatically grantedbased on a combination of user role, business workflow, and industryrequirements configured by an industry expert. In an example, a guidedpage may include a tabular view of data items, such that, the data itemsmay include actionable elements. Alternatively, a portion of data itemsmay include actionable elements. In another example, a guided page mayinclude a chart with chart elements that represent data associated witha data domain, such that, a portion of chart elements may includeactionable elements. In another example, a guided page may include oneof an access page, a central page, an overview page, and a list page.The access page may be associated with a core dimension. The centralpage may be associated with a central dimension. The central dimensionmay include a logical grouping of data in the data set around which coredimensions, dynamic dimensions, and data domains can be clustered.Alternatively, the central dimension may include a logical grouping ofdata that represents a subset of data associated with a core dimension.The overview page may be associated with a plurality of dimensions. Inan example, a user selection of an actionable element or point of entryon an access page results in presenting a guided page selected from theset consisting of access pages, central pages, and overview pages.Alternatively, a user selection of an actionable element or point ofentry on a central page results in presenting a guided page selectedfrom the set consisting of central pages, access pages, and list pages.Further, a user selection of an actionable element or point of entry ona list page results in presenting a guided page selected from the setconsisting of central pages, overview pages, and list pages. Moreover, auser selection of an actionable element or point of entry on an overviewpage results in presenting a guided page selected from the setconsisting of overview pages, access pages, and list pages.

In another aspect of the methods and systems described herein, a methodmay include using a processor to access a data set may include data froma plurality of data domains derived from a plurality ofindustry-specific data sources and a plurality of core dimensionsrelating to parameters of a business, wherein at least one of theplurality of core dimensions may be common to at least two of theplurality of data domains; facilitating direct navigation among theplurality of data domains in response to user interactions withdisplayable actionable elements presented in a first guided pageassociated a workflow of a business activity of the business, whereinuser interaction with one of the displayable actionable elements in thefirst guided page results in presenting a second guided page of a datadomain associated with the common core dimension other than the datadomain associated with the first guided page, and wherein the pluralityof guided pages comprise information determined by an industry expert tobe pertinent to operation of the business activity; facilitatingnavigation among the plurality of guided pages based on guided page highlevel description information accessible by the processor; and orientinginformation derived from the data in the data set in an interactiveelectronic display based on guided page data structure information thatmay be accessible by the processor.

The displayable actionable elements may be determined based on anindustry knowledge directed navigation map. Alternatively, thedisplayable actionable elements activate pathways to areas of interestbased on a workflow for a business activity associated with the dataset. In an example, data from any of the plurality of data domains thathave a common core dimension can be accessed via an actionable elementof a guided page that may include data of any of the plurality of datadomains that have the common core dimension. In an example, theplurality of guided pages may include groups of diverse-content pages ofa business presentation, navigation among the groups being based on acore dimension that may be common to the groups. The pages in a groupmay be independent of any specific data domain. In an example, the dataset may include data from a plurality of data domains may be derivedfrom a plurality of data sources including customer data sourcesassociated with operating a business and external data sources. Thecustomer data sources may include at least two sources selected from thelist consisting of flat files, spreadsheets, data warehouses, SQLdatabases, non-standard formatted data, legacy systems, transactionalsystems, and Enterprise Resource Planning (ERP) systems. The externaldata sources may include at least two sources selected from the listconsisting of third party feeds, market data, end-user data, state data,county data, regional data, and demo data. In an example, the pluralityof industry-specific core dimensions may include predetermined by anindustry-specific expert. In an example, a result of user interactionwith one of the displayable actionable elements may be predetermined byan industry-specific expert. Alternatively, a result of user interactionwith one of the displayable actionable elements may be determined by acombination of factors selected from the set consisting of: input froman industry-specific expert, a role of the user, a workflow of the user,previously presented guided pages, a core dimension associated with aselected actionable element or point of entry; business rules thatfacilitate configuring the plurality of data domains, candidate nextdomains, and metadata associated at least one of a presented guided pageand a selected actionable element or a point of entry. In an example,the data set may include calculated summaries of data suitable forpresentation in a guided page based on the guided page data structureinformation and non-summarized data that may be processed to comply withthe data structure information just prior to being presented in a guidedpage. Alternatively, the data set may include dimensions, summaryfields, and information fields. Further, the data set may includesummaries of all data associated with all common core dimensions.Moreover, the data set may include a plurality of core dimensions and aplurality of dynamic dimensions. In an example, presenting a guided pagebased on one of the plurality of dynamic dimensions may includeprocessing that data to conform with the guided page data structureinformation in response to a user interaction with one of thedisplayable actionable elements that may be not required for presentinga guided page based on one of the plurality of core dimensions. In anexample, the data set may include at least one central dimension. Acentral dimension may include a logical grouping of data in the data setaround which core dimensions, dynamic dimensions, and data domains canbe clustered. Alternatively, a central dimension may include a logicalgrouping of data that represents a subset of data associated with a coredimension. In an example, the data set may include data associated witha variety of access permissions that are automatically granted based onrelevancy of the data with at least one of the core dimensions of thedata set. The permissions are automatically granted based on acombination of user role, business workflow, and industry requirementsconfigured by an industry expert. In an example, a guided page mayinclude a tabular view of data items. The data items may includeactionable elements. Further, a portion of data items may includeactionable elements. In another example, a guided page may include achart with chart elements that represent data associated with a datadomain, such that, a portion of chart elements may include actionableelements. In another example, a guided page may include one of an accesspage, a central page, an overview page, and a list page. The access pagemay be associated with a core dimension. The central page may beassociated with a central dimension. The central dimension may include alogical grouping of data in the data set around which core dimensions,dynamic dimensions, and data domains can be clustered. Alternatively,the central dimension may include a logical grouping of data thatrepresents a subset of data associated with a core dimension. Theoverview page may be associated with a plurality of dimensions. In anexample, a user selection of an actionable element or point of entry onan access page results in presenting a guided page selected from the setconsisting of access pages, central pages, and overview pages.Alternatively, a user selection of an actionable element or point ofentry on a central page results in presenting a guided page selectedfrom the set consisting of central pages, access pages, and list pages.Further, a user selection of an actionable element or point of entry ona list page results in presenting a guided page selected from the setconsisting of central pages, overview pages, and list pages. Moreover, auser selection of an actionable element or point of entry on an overviewpage results in presenting a guided page selected from the setconsisting of overview pages, access pages, and list pages.

The methods and systems relating to creating a large number of reportsfor a data set. The reports may represent a wide range of possible viewsof the data set based on dimensions of data that may be present the dataset that may include a range of data sources used by a businessincluding internal and external data sources. The reports may havetables that include dimensions that may be calculated or dynamic, andvalues or summaries that correspond to those dimensions. In an example,values may be related to the dimensions to which they relate on thescreen as presented in a particular report. Further, values may berelated other dimensions in the collection of reports that are notshown. In an example, a user may select a dimension or a value. Theselection of a presented dimension may give the user the chance to see areport keyed to the presented dimension that may include values andpossibly other dimensions that may be associated with the selecteddimension. Further, selection of a value may allow a user to see otherdimensions that have a relationship with the selected value along withother values that are associated with the other dimensions. In anexample, pre-arranged, guided navigation may select what set of newdimensions and values will be shown based on a prescribed flow. The flowmay be iterative, such that a user can explore many values anddimensions by moving from value to value and dimension to dimension.

In an aspect of the methods and systems described herein, a system mayinclude a data set may include a plurality of industry-specific datasources and/or derived from a plurality of data sources used to operatea business including business-internal and external data sources; aplurality of guided pages representing a plurality of related views ofthe data set, wherein the plurality of guided pages are based ondimensions that are present in the data set; a plurality of tables thatcomprise the dimensions that are present in the data set, values thatcorrespond to dimensions that are common across a plurality of theguided pages, and summaries that correspond to the core dimensions,wherein the dimensions include core dimensions and dynamic dimensions; aplurality of actionable element data values that comprise actionableelements that are selectable by accessing the values; a plurality ofpoints of entry dimensions that comprise points of entry that areselectable by accessing the dimensions, wherein selection of a point ofentry in one of the plurality of guided pages results in automaticselection of a table of the plurality of tables that includes at leastone of values and at least one other dimension that may be associatedwith the selected point of entry dimension, and wherein selection of anactionable element in one of the plurality of guided pages results inautomatic selection of a table that includes at least one otherdimension that has a relationship with the selected actionable elementdata value and values that are associated with the at least one otherdimension; and a navigation map that facilitates determining which nextguided page, table, values, and dimensions to present in response to theselection of at least one of an actionable element and a point of entry,wherein the navigation map facilitates user exploration of a pluralityof values and a plurality of dimensions by allowing a user to move amongvalues and dimensions of the data set.

In an example, the selection may be performed by a single action in atouch screen user interface. The navigation map may be based on anindustry-specific business workflow and a selected guided page may bebased on the navigation map and a record of what pages a user haspreviously viewed. In another example, the selection of an actionableelement in a guided page may result in selection of a content itemselected from the group consisting of documentation, videos, manuals,sell sheets, images of hard copy documents including purchase orders,shipping documents, and product images. In an example, the plurality ofrelated views may include business-rules-based views. In an example, theplurality of guided pages may represent a plurality of related views ofthe data set are generated by applying business rules associated with aworkflow of a business to access of the plurality of industry-specificdata sources. The displayable actionable elements may be determinedbased on an industry knowledge directed navigation map. Alternatively,the displayable actionable elements may activate pathways to areas ofinterest based on a workflow for a business activity associated with thedata set. In an example, the points of entry are determined based on anindustry knowledge directed navigation map. Alternatively, the points ofentry activate pathways to areas of interest based on a workflow for thebusiness activity associated with the data set. In an example, a guidedpage that may be presented in response to user interaction with one ofthe displayable actionable elements may be based on a workflow for abusiness activity associated with the data set. The plurality of guidedpages may include groups of diverse-content pages of a businesspresentation, navigation among the groups being based on a coredimension that may be common to the groups. The pages in a group areindependent of any specific data domain. In an example, the customerdata sources comprise at least two sources selected from the listconsisting of flat files, spreadsheets, data warehouses, SQL databases,non-standard formatted data, legacy systems, transactional systems, andEnterprise Resource Planning (ERP) systems. In an example, the externaldata sources may include at least two sources selected from the listconsisting of third party feeds, market data, end-user data, state data,county data, regional data, and demo data. The plurality of dimensionsmay be industry-specific core dimensions and are predetermined by anindustry-specific expert. In an example, a result of user interactionwith one of the displayable actionable elements or points of entry maybe predetermined by an industry-specific expert. Alternatively, a resultof user interaction with one of the displayable actionable elements orpoints of entry may be determined by a combination of factors selectedfrom the set consisting of: input from an industry-specific expert, arole of the user, a workflow of the user, previously presented guidedpages, a core dimension associated with a selected actionable element orpoint of entry; business rules that facilitate configuring the pluralityof data domains, candidate next domains, and metadata associated atleast one of a presented guided page and a selected actionable elementor a point of entry. The data set may include calculated summaries ofdata suitable for presentation in a guided page based on the guided pagedata structure information and non-summarized data that may be processedto comply with the data structure information just prior to beingpresented in a guided page. In an example, the data set may includedimensions, summary fields, and information fields. Alternatively, thedata set may include summaries of all data associated with all commoncore dimensions. Further, the data set may include at least one centraldimension. A central dimension may include a logical grouping of data inthe data set around which core dimensions, dynamic dimensions, and datadomains can be clustered. Alternatively, a central dimension may includea logical grouping of data that represents a subset of data associatedwith a core dimension. In an example, the data set may include dataassociated with a variety of access permissions that are automaticallygranted based on relevancy of the data with at least one of the coredimensions of the data set. The permissions may be automatically grantedbased on a combination of user role, business workflow, and industryrequirements configured by an industry expert. In an example, a guidedpage may include a tabular view of data items. The guided page mayinclude points of entry as column and row headers of a matrix of dataitems. The data items may include actionable elements. In an example, aportion of data items may include actionable elements. In an example, aguided page may include a chart with chart elements that represent dataassociated with a data domain. In an example, a portion of chartelements may include actionable elements. In an example, a guided pagemay include one of an access page, a central page, an overview page, anda list page. The access page may be associated with a core dimension.The central page may be associated with a central dimension. The centraldimension may include a logical grouping of data in the data set aroundwhich core dimensions, dynamic dimensions, and data domains can beclustered. Alternatively, the central dimension may include a logicalgrouping of data that represents a subset of data associated with a coredimension. The overview page may be associated with a plurality ofdimensions. In an example, a user selection of an actionable element orpoint of entry on an access page results in presenting a guided pageselected from the set consisting of access pages, central pages, andoverview pages. Alternatively, a user selection of an actionable elementor point of entry on a central page results in presenting a guided pageselected from the set consisting of central pages, access pages, andlist pages. Further, a user selection of an actionable element or pointof entry on a list page results in presenting a guided page selectedfrom the set consisting of central pages, overview pages, and listpages. Moreover, a user selection of an actionable element or point ofentry on an overview page results in presenting a guided page selectedfrom the set consisting of overview pages, access pages, and list pages.

In another aspect of the methods and systems described herein, a methodmay include accessing with a processor a data set may include aplurality of industry-specific data domains derived from a plurality ofdata sources used to operate a business including business-internal andexternal data sources; generating with the processor a plurality ofguided pages representing a plurality of related views of the data set,wherein the plurality of guided pages are based on dimensions that arepresent in the data set; generating with a processor, for inclusion in aportion of the plurality of guided pages, a plurality of tables thatcomprise the dimensions that are present in the data set, values thatcorrespond to dimensions that are common across a plurality of theguided pages, and summaries that correspond to the core dimensions,wherein the dimensions include core dimensions and dynamic dimensions;configuring with the processor a plurality of actionable element datavalues that comprise actionable elements that are selectable byaccessing the values; generating with the processor a plurality ofpoints of entry dimensions that comprise points of entry that areselectable by accessing the dimensions, wherein selection of a point ofentry in one of the plurality of guided pages results in automaticselection of a table of the plurality of tables that includes at leastone of values and at least one other dimension that may be associatedwith the selected point of entry dimension, and wherein selection of anactionable element in one of the plurality of guided pages results inautomatic selection of a table that includes at least one otherdimension that has a relationship with the selected actionable elementdata value and values that are associated with the at least one otherdimension; and referencing with the processor a navigation map todetermine which next guided page, table, values, and dimensions topresent in response to the selection of at least one of an actionableelement and a point of entry, wherein the navigation map facilitatesuser exploration of a plurality of values and a plurality of dimensionsby allowing a user to move among values and dimensions of the data set.

The displayable actionable elements may be determined based on anindustry knowledge directed navigation map. The displayable actionableelements may activate pathways to areas of interest based on a workflowfor a business activity associated with the data set. The points ofentry activate pathways to areas of interest based on a workflow for thebusiness activity associated with the data set. In an example, a guidedpage that may be presented in response to user interaction with one ofthe displayable actionable elements or points of entry may be based on aworkflow for a business activity associated with the data set. Theplurality of guided pages may include groups of diverse-content pages ofa business presentation, navigation among the groups being based on acore dimension that may be common to the groups. In an example, thepages in a group are independent of any specific data domain. In anexample, the data set may include data from a plurality of data domainsmay be derived from a plurality of data sources including customer datasources associated with operating a business and external data sources.The customer data sources comprise at least two sources selected fromthe list consisting of flat files, spreadsheets, data warehouses, SQLdatabases, non-standard formatted data, legacy systems, transactionalsystems, and Enterprise Resource Planning (ERP) systems. The externaldata sources comprise at least two sources selected from the listconsisting of third party feeds, market data, end-user data, state data,county data, regional data, and demo data. In an example, a result ofuser interaction with one of the displayable actionable elements orpoints of entry may be predetermined by an industry-specific expert.Alternatively, a result of user interaction with one of the displayableactionable elements or points of entry may be determined by acombination of factors selected from the set consisting of: input froman industry-specific expert, a role of the user, a workflow of the user,previously presented guided pages, a core dimension associated with aselected actionable element or point of entry; business rules thatfacilitate configuring the plurality of data domains, candidate nextdomains, and metadata associated at least one of a presented guided pageand a selected actionable element or a point of entry. The data set mayinclude calculated summaries of data suitable for presentation in aguided page based on the guided page data structure information andnon-summarized data that may be processed to comply with the datastructure information just prior to being presented in a guided page. Inan example, the data set may include dimensions, summary fields, andinformation fields. Alternatively, the data set may include summaries ofall data associated with all common core dimensions. Further, the dataset may include a plurality of core dimensions and a plurality ofdynamic dimensions. In an example, presenting a guided page based on oneof the plurality of dynamic dimensions may include processing that datato conform with the guided page data structure information in responseto a user interaction with one of the displayable actionable elements orpoints of entry that may be not required for presenting a guided pagebased on one of the plurality of core dimensions. In an example, thedata set may include at least one central dimension. A central dimensionmay include a logical grouping of data in the data set around which coredimensions, dynamic dimensions, and data domains can be clustered.Alternatively, a central dimension may include a logical grouping ofdata that represents a subset of data associated with a core dimension.In an example, the data set may include data associated with a varietyof access permissions that are automatically granted based on relevancyof the data with at least one of the core dimensions of the data set.The permissions are automatically granted based on a combination of userrole, business workflow, and industry requirements configured by anindustry expert. In an example, a guided page may include a tabular viewof data items. The guided page may include points of entry as column androw headers of a matrix of data items. The data items may includeactionable elements. Alternatively, a portion of data items may includeactionable elements. In an example, a guided page may include a chartwith chart elements that represent data associated with a data domain,such that, a portion of chart elements comprise actionable elements. Inanother example, a guided page may include one of an access page, acentral page, an overview page, and a list page. The access page may beassociated with a core dimension. The central page may be associatedwith a central dimension. The central dimension may include a logicalgrouping of data in the data set around which core dimensions, dynamicdimensions, and data domains can be clustered. Alternatively, thecentral dimension may include a logical grouping of data that representsa subset of data associated with a core dimension. The overview page maybe associated with a plurality of dimensions. In an example, a userselection of an actionable element or point of entry on an access pageresults in presenting a guided page selected from the set consisting ofaccess pages, central pages, and overview pages. Alternatively, a userselection of an actionable element or point of entry on a central pageresults in presenting a guided page selected from the set consisting ofcentral pages, access pages, and list pages. Further, a user selectionof an actionable element or point of entry on a list page results inpresenting a guided page selected from the set consisting of centralpages, overview pages, and list pages. Moreover, a user selection of anactionable element or point of entry on an overview page results inpresenting a guided page selected from the set consisting of overviewpages, access pages, and list pages.

A method may relate to generating an industry-specific data model oftime-period based summaries of data of core data dimensions that aredetermined in a discovery process of data in a data set. The time-periodbased summaries may be generated for each of a plurality of coredimensions.

In an aspect of the methods and systems described herein, a methodinclude gathering data from a plurality of data sources includinginternal data sources associated with operating a business and externaldata sources; integrating the gathered data with a script-basedintegrator; determining core dimensions of the gathered data;calculating one or more time-based summaries of data associated witheach core dimension; and organizing the time-based summaries into aplurality of distinct model-based data repositories.

In an example, a script for the script-based integrator may be createdusing a visual integration facility. The internal data sources mayinclude at least two sources selected from the list consisting of flatfiles, spreadsheets, data warehouses, SQL databases, non-standardformatted data, legacy systems, transactional systems, and EnterpriseResource Planning (ERP) systems. The external data sources may includeat least two sources selected from the list consisting of third partyfeeds, market data, end-user data, state data, county data, regionaldata, and demo data. In an example, the method may further includeconfiguring a set of business rules for accessing the model-based datarepositories and attaching each business rule in the set of businessrules for a specific model-based data repository to the specific datarepository. Further, the method may include generating a set of guidedpages that facilitate navigation in and among a plurality of distinctdata domains that are clustered around the core dimensions, wherein theset of guided pages includes pages that comprise the time-basedsummaries.

The methods and systems relating to associating documents to at leastone core dimension and to data clustered around the core dimension sothat the documents can be viewed in a tablet-based user interface inresponse to a user selection of an actionable item in the interface.

In an aspect of the methods and systems described herein, a system mayinclude a data set may include data from a plurality of data domains,the data set characterized by a plurality of industry-specific coredimensions, wherein at least one of the plurality of industry-specificcore dimensions may be common to at least two of the plurality of datadomains; a plurality of document links, wherein each of the plurality ofdocument links may be associated with at least one data domain and atleast one common core dimension shared by at least a pair of documentswithin the at least one data domain; a plurality of guided pages thatfacilitate access to documents in a tablet-based user interface throughuser interactions with displayable actionable elements and points ofentry associated with the industry-specific core dimensions, whereinuser interaction with one of the displayable actionable elements resultsin accessing a document link that facilitates presenting a document thatmay be related to a data domain with which the actionable element may beassociated, and wherein user interaction with one of the displayablepoints of entry results in accessing a document link that facilitatespresenting a document that may be related to an industry-specific coredimension with which the point of entry may be associated; guided pagehigh level description information that facilitates navigation among theplurality of guided pages and documents; and guided page data structureinformation that facilitates orienting information derived from the datain the data set in an interactive electronic display.

In an example, the system may further include a document managementsystem organized by at least one of industry, roles of users,company-specific requirements, and product placement. The plurality ofguided pages may include groups of diverse-content pages of a businesspresentation, navigation among the groups being based on a coredimension that may be common to the groups. The selection of a point ofentry or an actionable element in a guided page may result in selectionof one of the set of content items consisting of documentation, videos,manuals, sell sheets, images of hard copy documents including purchaseorders, shipping documents, and product images. The displayableactionable elements may be determined based on an industry knowledgedirected navigation map. Further, the displayable actionable elementsactivate pathways to areas of interest based on a workflow for abusiness activity associated with the data set. The points of entry aredetermined based on an industry knowledge directed navigation map.Further, the points of entry may activate pathways to areas of interestbased on a workflow for the business activity associated with the dataset. In an example, data from any of the plurality of data domains thathave a common core dimension can be accessed via an actionable elementof a guided page that may include data of any of the plurality of datadomains that have the common core dimension. In an example, a guidedpage that may be presented in response to user interaction with one ofthe displayable actionable elements or points of entry may be based on aworkflow for a business activity associated with the data set. Theplurality of guided pages may include groups of diverse-content pages ofa business presentation, navigation among the groups being based on acore dimension that may be common to the groups. The pages in a groupmay be independent of any specific data domain. In an example, the dataset may include data from a plurality of data domains may be derivedfrom a plurality of data sources including customer data sourcesassociated with operating a business and external data sources. Thecustomer data sources comprise at least two sources selected from thelist consisting of flat files, spreadsheets, data warehouses, SQLdatabases, non-standard formatted data, legacy systems, transactionalsystems, and Enterprise Resource Planning (ERP) systems. The externaldata sources may include at least two sources selected from the listconsisting of third party feeds, market data, end-user data, state data,county data, regional data, and demo data. In an example, the pluralityof industry-specific core dimensions may be predetermined by anindustry-specific expert. In an example, a result of user interactionwith one of the displayable actionable elements or points of entry maybe predetermined by an industry-specific expert. Alternatively, a resultof user interaction with one of the displayable actionable elements orpoints of entry may be determined by a combination of factors selectedfrom the set consisting of: input from an industry-specific expert, arole of the user, a workflow of the user, previously presented guidedpages, a core dimension associated with a selected actionable element orpoint of entry; business rules that facilitate configuring the pluralityof data domains, candidate next domains, and metadata associated atleast one of a presented guided page and a selected actionable elementor a point of entry. The data set may include calculated summaries ofdata suitable for presentation in a guided page based on the guided pagedata structure information and non-summarized data that may be processedto comply with the data structure information just prior to beingpresented in a guided page. Alternatively, the data set may includedimensions, summary fields, and information fields. Further, the dataset may include summaries of all data associated with all common coredimensions. Moreover, the data set may include a plurality of coredimensions and a plurality of dynamic dimensions. In an example, aguided page may be presented based on one of the plurality of dynamicdimensions may include processing that data to conform with the guidedpage data structure information in response to a user interaction withone of the displayable actionable elements or points of entry that maybe not required for presenting a guided page based on one of theplurality of core dimensions. In an example, the data set may include atleast one central dimension. The central dimension may include a logicalgrouping of data in the data set around which core dimensions, dynamicdimensions, and data domains can be clustered. Alternatively, a centraldimension may include a logical grouping of data that represents asubset of data associated with a core dimension. In an example, the dataset may include data associated with a variety of access permissionsthat are automatically granted based on relevancy of the data with atleast one of the core dimensions of the data set. The permissions may beautomatically granted based on a combination of user role, businessworkflow, and industry requirements configured by an industry expert. Inan example, a guided page may include a tabular view of data items. Theguided page may include points of entry as column and row headers of amatrix of data items. The data items may include actionable elements.Alternatively, a portion of data items may include actionable elements.In an example, a guided page may include a chart with chart elementsthat represent data associated with a data domain, such that, a portionof chart elements may include actionable elements. In another example, aguided page may include one of an access page, a central page, anoverview page, and a list page. The access page may be associated with acore dimension. The central page may be associated with a centraldimension. The central dimension may include a logical grouping of datain the data set around which core dimensions, dynamic dimensions, anddata domains can be clustered. Alternatively, the central dimension mayinclude a logical grouping of data that represents a subset of dataassociated with a core dimension. The overview page may be associatedwith a plurality of dimensions. In an example, a user selection of anactionable element or point of entry on an access page results inpresenting a guided page selected from the set consisting of accesspages, central pages, and overview pages. Alternatively, a userselection of an actionable element or point of entry on a central pageresults in presenting a guided page selected from the set consisting ofcentral pages, access pages, and list pages. Further, a user selectionof an actionable element or point of entry on a list page results inpresenting a guided page selected from the set consisting of centralpages, overview pages, and list pages. Moreover, a user selection of anactionable element or point of entry on an overview page results inpresenting a guided page selected from the set consisting of overviewpages, access pages, and list pages.

In another aspect of the methods and systems described herein, a methodmay include using a processor to access a data set may include data froma plurality of data domains, a plurality of industry-specific coredimensions, wherein at least one of the plurality of industry-specificcore dimensions may be common to at least two of the plurality of datadomains, and a plurality of document links, wherein each of theplurality of document links may be associated with at least one datadomain and at least one common core dimension shared by at least a pairof documents within the at least one data domain; facilitating access todocuments via a plurality of guided pages in a tablet-based userinterface through user interactions with displayable actionable elementsor points of entry associated with the industry-specific core dimensionspresented in the plurality of guided pages, wherein user interactionwith one of the displayable actionable elements results in accessing adocument link that facilitates presenting a document that may be relatedto a data domain with which the actionable element may be associated,and wherein user interaction with one of the displayable points of entryresults in accessing a document link that facilitates presenting adocument that may be related to an industry-specific core dimension withwhich the point of entry may be associated; facilitating navigationamong the plurality of guided pages based on guided page high leveldescription information accessible by the processor; and orientinginformation derived from the data in the data set in an interactiveelectronic display based on guided page data structure information thatmay be accessible by the processor.

The displayable actionable elements may be determined based on anindustry knowledge directed navigation map. Further, the displayableactionable elements may activate pathways to areas of interest based ona workflow for a business activity associated with the data set. Thepoints of entry may be determined based on an industry knowledgedirected navigation map. Further, the points of entry may activatepathways to areas of interest based on a workflow for the businessactivity associated with the data set. In an example, data from any ofthe plurality of data domains that have a common core dimension may beaccessed via an actionable element of a guided page that may includedata of any of the plurality of data domains that have the common coredimension. In an example, a guided page presented in response to userinteraction with one of the displayable actionable elements or points ofaccess may be based on a workflow for a business activity associatedwith the data set. In an example, the plurality of guided pages mayinclude groups of diverse-content pages of a business presentation,navigation among the groups being based on a core dimension that may becommon to the groups. The pages in a group may be independent of anyspecific data domain. In an example, the data set may include data froma plurality of data domains derived from a plurality of data sourcesincluding customer data sources associated with operating a business andexternal data sources. The customer data sources may include at leasttwo sources selected from the list consisting of flat files,spreadsheets, data warehouses, SQL databases, non-standard formatteddata, legacy systems, transactional systems, and Enterprise ResourcePlanning (ERP) systems. The external data sources may include at leasttwo sources selected from the list consisting of third party feeds,market data, end-user data, state data, county data, regional data, anddemo data. In an example, the plurality of industry-specific coredimensions may be predetermined by an industry-specific expert. In anexample, a result of user interaction with one of the displayableactionable elements or points of entry may be predetermined by anindustry-specific expert. Alternatively, a result of user interactionwith one of the displayable actionable elements or points of entry maybe determined by a combination of factors selected from the setconsisting of: input from an industry-specific expert, a role of theuser, a workflow of the user, previously presented guided pages, a coredimension associated with a selected actionable element or point ofentry; business rules that facilitate configuring the plurality of datadomains, candidate next domains, and metadata associated at least one ofa presented guided page and a selected actionable element or a point ofentry. In an example, the data set may include calculated summaries ofdata suitable for presentation in a guided page based on the guided pagedata structure information and non-summarized data that may be processedto comply with the data structure information just prior to beingpresented in a guided page. In another example, the data set may includedimensions, summary fields, and information fields. Further, the dataset may include summaries of all data associated with all common coredimensions. Moreover, the data set may include a plurality of coredimensions and a plurality of dynamic dimensions. A guided page may bepresented based on one of the plurality of dynamic dimensions mayinclude processing that data to conform with the guided page datastructure information in response to a user interaction with one of thedisplayable actionable elements or points of entry that may be notrequired for presenting a guided page based on one of the plurality ofcore dimensions. In an example, the data set may include at least onecentral dimension. A central dimension may include a logical grouping ofdata in the data set around which core dimensions, dynamic dimensions,and data domains can be clustered. Alternatively, a central dimensionmay include a logical grouping of data that represents a subset of dataassociated with a core dimension. In an example, the data set mayinclude data associated with a variety of access permissions that areautomatically granted based on relevancy of the data with at least oneof the core dimensions of the data set. The permissions may beautomatically granted based on a combination of user role, businessworkflow, and industry requirements configured by an industry expert. Inan example, a guided page may include a tabular view of data items, suchthat the guided page may include points of entry as column and rowheaders of a matrix of data items. The data items may include actionableelements. Alternatively, a portion of data items may include actionableelements. In an example, a guided page may include a chart with chartelements that represent data associated with a data domain. Further, aportion of chart elements may include actionable elements. In anexample, a guided page may include one of an access page, a centralpage, an overview page, and a list page. The access page may beassociated with a core dimension. The central page may be associatedwith a central dimension. The central dimension may include a logicalgrouping of data in the data set around which core dimensions, dynamicdimensions, and data domains can be clustered. Alternatively, thecentral dimension may include a logical grouping of data that representsa subset of data associated with a core dimension. The overview page maybe associated with a plurality of dimensions. In an example, a userselection of an actionable element or point of entry on an access pageresults in presenting a guided page selected from the set consisting ofaccess pages, central pages, and overview pages. Alternatively, a userselection of an actionable element or point of entry on a central pageresults in presenting a guided page selected from the set consisting ofcentral pages, access pages, and list pages. Further, a user selectionof an actionable element or point of entry on a list page results inpresenting a guided page selected from the set consisting of centralpages, overview pages, and list pages. Moreover, a user selection of anactionable element or point of entry on an overview page results inpresenting a guided page selected from the set consisting of overviewpages, access pages, and list pages.

The methods and systems relating to creating a large number of reportsfor a data set. The reports represent a wide range of possible views ofthe data set based on dimensions of data that may be present the dataset that comprises a range of data sources used by a business includinginternal and external data sources. The reports may have tables thathave dimensions that may be calculated or dynamic, and values orsummaries that correspond to those dimensions. The values may be relatedto the dimensions to which they relate on the screen as presented in aparticular report. Further, values may include other dimensions in thecollection of reports that are not shown. In an example, a user canselect a dimension or a value. The selection of a presented dimensiongives the user the chance to see a report keyed to the presenteddimension that includes values and possibly other dimensions that areassociated with the selected dimension. Further, selection of a valuemay allow a user to see other dimensions that have a relationship withthe selected value along with other values that are associated with theother dimensions. In an example, pre-arranged, guided navigation selectswhat set of new dimensions and values will be shown based on aprescribed flow.

In an example, the system may include a data set including a pluralityof industry-specific data domains derived from a plurality of datasources used to operate a business including business-internal andexternal data sources. The system may further include a plurality ofguided pages representing a plurality of related views of the data set,wherein the plurality of guided pages may be based on dimensions thatmay be present in the data set. The system may further include aplurality of tables that may include the dimensions that may be presentin the data set, values that may correspond to dimensions that may becommon across a plurality of the guided pages, and summaries that maycorrespond to the core dimensions. The dimensions may include coredimensions and dynamic dimensions. The system may further include aplurality of actionable element data values that may include actionableelements that are may be selectable by accessing the values. The systemmay further include a plurality of points of entry dimensions that mayinclude points of entry that may be selectable by accessing thedimensions. The selection of a point of entry in one of the plurality ofguided pages may result in automatic selection of a table of theplurality of tables that may include at least one of values and at leastone other dimension that may be associated with the selected point ofentry dimension, and wherein selection of an actionable element in oneof the plurality of guided pages may result in automatic selection of atable that may include at least one other dimension that may have arelationship with the selected actionable element data value and valuesthat may be associated with the at least one other dimension. The systemmay further include a navigation map that may facilitate determiningwhich next guided page, table, values, and dimensions to present inresponse to the selection of at least one of an actionable element and apoint of entry.

The displayable actionable elements may be determined based on anindustry knowledge directed navigation map. The displayable actionableelements may activate pathways to areas of interest based on a workflowfor a business activity associated with the data set. The points ofentry may be determined based on an industry knowledge directednavigation map. The points of entry may activate pathways to areas ofinterest based on a workflow for the business activity associated withthe data set. A guided page that is presented in response to userinteraction with one of the displayable actionable elements or points ofentry may be based on a workflow for a business activity associated withthe data set. The plurality of guided pages may include groups ofdiverse-content pages of a business presentation, navigation among thegroups being based on a core dimension that is common to the groups. Thepages in a group may be independent of any specific data domain. Thedata set may include data from a plurality of data domains and isderived from a plurality of data sources including customer data sourcesassociated with operating a business and external data sources. Thecustomer data sources may include at least two sources selected from thelist consisting of flat files, spreadsheets, data warehouses, SQLdatabases, non-standard formatted data, legacy systems, transactionalsystems, and Enterprise Resource Planning (ERP) systems. The externaldata sources may include at least two sources selected from the listconsisting of third party feeds, market data, end-user data, state data,county data, regional data, and demo data. The plurality ofindustry-specific core dimensions may be predetermined by anindustry-specific expert. A result of user interaction with one of thedisplayable actionable elements or points of entry may be predeterminedby an industry-specific expert. In an example, a result of userinteraction with one of the displayable actionable elements or points ofentry may be determined by a combination of factors selected from theset consisting of: input from an industry-specific expert, a role of theuser, a workflow of the user, previously presented guided pages, a coredimension associated with a selected actionable element or point ofentry; business rules that facilitate configuring a plurality of datadomains, candidate next domains, and metadata associated at least one ofa presented guided page and a selected actionable element or a point ofentry. The data set may include calculated summaries of data suitablefor presentation in a guided page based on the guided page datastructure information and non-summarized data that may be processed tocomply with the data structure information just prior to being presentedin a guided page. The data set may include dimensions, summary fields,and information fields. The data set may include summaries of all dataassociated with all common core dimensions. The data set may include aplurality of core dimensions and a plurality of dynamic dimensions. Thepresenting of a guided page based on one of the plurality of dynamicdimensions may include processing that data to conform with the guidedpage data structure information in response to a user interaction withone of the displayable actionable elements or points of entry that maynot be required for presenting a guided page based on one of theplurality of core dimensions. The data set may include at least onecentral dimension. A central dimension may include a logical grouping ofdata in the data set around which core dimensions, dynamic dimensions,and data domains may be clustered. In an example, a central dimensionmay include a logical grouping of data that may represent a subset ofdata associated with a core dimension. The data set may include dataassociated with a variety of access permissions that may beautomatically granted based on relevancy of the data with at least oneof the core dimensions of the data set. The permissions may beautomatically granted based on a combination of user role, businessworkflow, and industry requirements configured by an industry expert. Inan example, a guided page may include a tabular view of data items. Theguided page may include points of entry as column and row headers of amatrix of data items. The data items may include actionable elements. Aportion of data items may include actionable elements. A guided page mayinclude a chart with chart elements that may represent data associatedwith a data domain. A portion of chart elements may include actionableelements. A guided page may include one of an access page, a centralpage, an overview page, and a list page. The access page may beassociated with a core dimension. The central page may be associatedwith a central dimension. The central dimension may include a logicalgrouping of data in the data set around which core dimensions, dynamicdimensions, and data domains can be clustered. The central dimension mayinclude a logical grouping of data that represents a subset of dataassociated with a core dimension. The overview page may be associatedwith a plurality of dimensions. In an example, a user selection of anactionable element or point of entry on an access page may result inpresenting a guided page selected from the set consisting of accesspages, central pages, and overview pages. A user selection of anactionable element or point of entry on a central page may result inpresenting a guided page selected from the set consisting of centralpages, access pages, and list pages. In an example, a user selection ofan actionable element or point of entry on a list page may result inpresenting a guided page selected from the set consisting of centralpages, overview pages, and list pages. In an example, a user selectionof an actionable element or point of entry on an overview page mayresult in presenting a guided page selected from the set consisting ofoverview pages, access pages, and list pages.

In an example, a method is disclosed that includes accessing with aprocessor a data set comprising a plurality of industry-specific datadomains derived from a plurality of data sources used to operate abusiness including business-internal and external data sources. Themethod may further include generating with the processor a plurality ofguided pages representing a plurality of related views of the data set,wherein the plurality of guided pages may be based on dimensions thatmay be present in the data set. The method may include generating with aprocessor, for inclusion in a portion of the plurality of guided pages,a plurality of tables that may include the dimensions that may bepresent in the data set, values that may correspond to dimensions thatmay be common across a plurality of the guided pages, and summaries thatmay correspond to the core dimensions. The dimensions may include coredimensions and dynamic dimensions. The method may include configuringwith the processor a plurality of actionable element data values thatmay include actionable elements that may be selectable by accessing thevalues. The method may include generating with the processor a pluralityof points of entry dimensions that may include points of entry that areselectable by accessing the dimensions. The selection of a point ofentry in one of the plurality of guided pages may result in automaticselection of a table of the plurality of tables that may include atleast one of values and at least one other dimension that may beassociated with the selected point of entry dimension, and whereinselection of an actionable element in one of the plurality of guidedpages may result in automatic selection of a table that may include atleast one other dimension that may have a relationship with the selectedactionable element data value and values that may be associated with theat least one other dimension. The method may include referencing withthe processor a navigation map to determine which next guided page,table, values, and dimensions to present in response to the selection ofat least one of an actionable element and a point of entry.

The displayable actionable elements may be determined based on anindustry knowledge directed navigation map. The displayable actionableelements may activate pathways to areas of interest based on a workflowfor a business activity associated with the data set. The points ofentry may be determined based on an industry knowledge directednavigation map. The method points of entry may activate pathways toareas of interest based on a workflow for the business activityassociated with the data set. A guided page that is presented inresponse to user interaction with one of the displayable actionableelements or points of entry may be based on a workflow for a businessactivity associated with the data set. The plurality of guided pages mayinclude groups of diverse-content pages of a business presentation,navigation among the groups being based on a core dimension that iscommon to the groups. The pages in a group may be independent of anyspecific data domain. The data set may include data from a plurality ofdata domains and may be derived from a plurality of data sourcesincluding customer data sources associated with operating a business andexternal data sources. The customer data sources may include at leasttwo sources selected from the list consisting of flat files,spreadsheets, data warehouses, SQL databases, non-standard formatteddata, legacy systems, transactional systems, and Enterprise ResourcePlanning (ERP) systems. The external data sources may include at leasttwo sources selected from the list consisting of third party feeds,market data, end-user data, state data, county data, regional data, anddemo data. The plurality of industry-specific core dimensions may bepredetermined by an industry-specific expert. In an example, a result ofuser interaction with one of the displayable actionable elements orpoints of entry may be predetermined by an industry-specific expert. Inan example, a result of user interaction with one of the displayableactionable elements or points of entry may be determined by acombination of factors selected from the set consisting of: input froman industry-specific expert, a role of the user, a workflow of the user,previously presented guided pages, a core dimension associated with aselected actionable element or point of entry; business rules that mayfacilitate configuring a plurality of data domains, candidate nextdomains, and metadata associated at least one of a presented guided pageand a selected actionable element or a point of entry. The data set mayinclude calculated summaries of data suitable for presentation in aguided page based on the guided page data structure information andnon-summarized data that may be processed to comply with the datastructure information just prior to being presented in a guided page.The data set may include dimensions, summary fields, and informationfields. The data set may include summaries of all data associated withall common core dimensions. The data set may include a plurality of coredimensions and a plurality of dynamic dimensions. The method ofpresenting a guided page based on one of the plurality of dynamicdimensions may include processing that data to conform with the guidedpage data structure information in response to a user interaction withone of the displayable actionable elements or points of entry that maynot be required for presenting a guided page based on one of theplurality of core dimensions. The data set may include at least onecentral dimension. In an example, a central dimension may include alogical grouping of data in the data set around which core dimensions,dynamic dimensions, and data domains may be clustered. In an example, acentral dimension may include a logical grouping of data that mayrepresent a subset of data associated with a core dimension. The dataset may include data associated with a variety of access permissionsthat may be automatically granted based on relevancy of the data with atleast one of the core dimensions of the data set. The permissions may beautomatically granted based on a combination of user role, businessworkflow, and industry requirements configured by an industry expert. Inan example, a guided page may include a tabular view of data items. Theguided page may include points of entry as column and row headers of amatrix of data items. The data items may include actionable elements. Aportion of data items may include actionable elements. In an example, aguided page may include a chart with chart elements that may representdata associated with a data domain. A portion of chart elements mayinclude actionable elements. In an example, a guided page may includeone of an access page, a central page, an overview page, and a listpage. The access page may be associated with a core dimension. Thecentral page may be associated with a central dimension. The centraldimension may include a logical grouping of data in the data set aroundwhich core dimensions, dynamic dimensions, and data domains may beclustered. The central dimension may include a logical grouping of datathat may represent a subset of data associated with a core dimension.The overview page may be associated with a plurality of dimensions. Inan example, a user selection of an actionable element or point of entryon an access page may result in presenting a guided page selected fromthe set consisting of access pages, central pages, and overview pages.In an example, a user selection of an actionable element or point ofentry on a central page may result in presenting a guided page selectedfrom the set consisting of central pages, access pages, and list pages.In an example, a user selection of an actionable element or point ofentry on a list page may result in presenting a guided page selectedfrom the set consisting of central pages, overview pages, and listpages. In an example, a user selection of an actionable element or pointof entry on an overview page may result in presenting a guided pageselected from the set consisting of overview pages, access pages, andlist pages.

The methods and systems relating to updating a set of guided pages foroperation of a business activity based on a workflow for the activity,industry expertise, and source data that may be relevant to the businessactivity. A point of entry to access the guided pages may be organizedaround one or more industry-specific data dimensions. A client-serversynchronization may be updated may be based on per-guided page cachetags managed and exchanged between client and server duringsynchronization.

In an aspect of the methods and systems described herein, a methodincludes generating with a server a plurality of guided pages foroperation of a business activity based on a workflow for the activity,industry expertise, and source data that may be relevant to the businessactivity. The points of entry to navigate among the guided pages may beorganized around one or more industry-specific data dimensions. Themethod further includes associating a cache tag with each page of theplurality of guided pages. The method further includes transmitting thegenerated plurality of guided pages and the associated cache tags to aclient device during a first synchronization operation. The methodfurther includes updating a portion of the plurality of guided pageswith the server and generating a set of updated cache tags for theupdated guided pages. The method further includes receiving cache tagsfrom the client device that indicate the guided pages that may bepresent on the client device. The method further includes comparing thereceived cache tags with the set of updated cache tags to determineupdated guided pages that may be not present on the client. The methodfurther includes transmitting the determined updated guided pages and anupdated cache tag for each of the determined updated guided pages to theclient during a second synchronization operation.

In addition, the cache tags received from the client device includescache tags of requested guided pages. The step of comparing with theserver may receive cache tags to cache tags transmitted to the clientdevice and transmitting guided pages for the received cache tags that donot match to any of the cache tags transmitted to the client device. Thecache tags received from the client device includes cache tags of guidedpages that may have changed on the client device since the lastsynchronization operation. Alternatively, the step of comparing with theserver may receive cache tags to cache tags transmitted to the clientdevice and receiving guided pages for the received cache tags thatindicate that the guided pages have changed on the client device sincethe last synchronization operation. Alternatively, the step of comparingwith the server may receive cache tags to cache tags transmitted to theclient device and receiving guided pages for the received cache tagsthat indicate that the guided pages have changed on the client devicesince the last synchronization operation.

The methods and systems relating to presenting alternate views ofsummarized data that include enabling selection among a variety ofdimensions of the summarized data.

In an aspect of the methods and systems described herein, the methodincludes using a processor to present a guided page with a summarycolumn of data from a data set including data from a plurality of datadomains and a plurality of industry-specific core dimensions. The atleast one of the plurality of industry-specific core dimensions may becommon to at least two of the plurality of data domains. The methodfurther includes facilitating data discovery through user interactionswith a column heading of the summary column that may be presented by theprocessor on an interactive electronic display. The user interactionwith the column heading results in expanding the summary column topresent an alternate view of the data in the data set that may besummarized in the summary column. The alternate view may be selectablefrom a plurality of data domains that share the common industry-specificcore dimension.

In an aspect of the methods and systems described herein, describedherein may be a method including facilitating data discovery throughuser interactions with a column heading of the summary column that maybe presented by the processor on an interactive electronic display. Theuser interaction with the column heading results in expanding thesummary column to present an alternate view of the data in the data setthat may be summarized in the summary column. The alternate view may beselectable from a plurality of data domains that share the commonindustry-specific core dimension.

In addition, the guided page may be one of an access page and a listpage. The method further includes expanding the summary column topresent an alternate view of the data in the data set may be performedwithout requiring access to the data set. The alternate view may befurther selectable from a plurality of time-based subsets of thesummarized data. In addition, a selected time-based subset may bepresented in the expanded column without requiring access to the dataset.

The methods and systems relating to configuring a set of guided pagesfor operation of a business activity based on a workflow for theactivity, industry expertise, and source data that may be relevant tothe business activity. Each page of the set of guided pages may beorganized around one or more industry-specific data dimension.

In an aspect of the methods and systems described herein, describedherein may be a method using a processor to access a data set includingdata from a plurality of data domains derived from a plurality ofdimensions of a business. The at least one of the plurality ofdimensions may be common to at least two of the plurality of datadomains. The method further includes generating with a processor aplurality of guided pages that may include data from at least one of aplurality of data domains, actionable elements and points of entry beingassociated with a workflow of a business activity of the business andfacilitating presenting other guided pages including at least one ofdata related to at least one of the plurality of data domains and datarelated to a common dimension. The plurality of guided pages includesinformation may be determined by an industry expert to be pertinent tooperation of the business activity. The method includes processingguided page high level description information to facilitate navigationamong the plurality of guided pages. The method further includesorienting information derived from the data in the data set in aninteractive electronic display based on guided page data structureinformation that may be accessible by the processor.

The columnar-based data set may be structured for rapid access. Therapid access may be performed by a diver facility. The at least one ofthe one or more common dimensions may be a core data dimension. The atleast one of the one or more common dimensions may be a dynamicdimension. The at least one page of the plurality of guided pages may bea presentation object. The presentation object may be a portion of adocument.

The methods and systems relating to configuring a set of guideddocuments for operation of a business activity based on a workflow forthe activity, industry expertise, and source data that may be relevantto the business activity. In an example, a point of entry to access theguided documents may be organized around one or more industry-specificdata dimensions.

In an aspect of the methods and systems described herein, describedherein may be a method using a processor to access a data set includingdata from a plurality of data domains, a plurality of industry-specificdimensions, wherein at least one of the plurality of industry-specificdimensions may be common to at least two of the plurality of datadomains, and a plurality of document links. The each of the plurality ofdocument links may be associated with at least one data domain and atleast one common dimension. The method further includes generating aplurality of guided pages, a plurality of displayable actionableelements, and a plurality of points of entry associated with theindustry-specific dimensions. The displayable actionable elementsfacilitate accessing a document link that facilitates presenting adocument that may be related to a data domain with which the actionableelement may be associated. The displayable points of entry facilitateaccessing a document link that facilitates presenting a document thatmay be related to an industry-specific dimension with which the point ofentry may be associated. The method further includes processing guidedpage high level description information to facilitate navigation amongthe plurality of guided pages. The method further includes orientinginformation derived from the data in the data set for presentation in aninteractive electronic display based on guided page data structureinformation that may be accessible by the processor.

The methods and systems relating to configuring navigation map fornavigation among a set of guided pages for operation of a businessactivity allowing freeform data discovery. The methods and systems maybe based on a modeling method including multi-source data collection,script-based data integration, industry-specific core data dimensiondiscovery; data modeling to provide rapid access to domains of data thatmay be relevant to the business activity; and industry-specific businessrules.

In an aspect of the methods and systems described herein, describedherein may be a system of preconfigured page-to-page navigation. Thesystem may include a data set derived through a modeling process thatincludes integration of data sourced from a plurality of data sourcedinto a columnar-based data set that may be relevant to a businessactivity, the data set characterized by data having a plurality ofindustry-specific core dimensions. The at least one of the plurality ofindustry-specific core dimensions may be common to at least two of aplurality of data domains derived from the domain-specific data models.The system further includes a navigation map for facilitating navigationamong a set of guided pages representing a plurality of related views ofthe data set and based on dimensions that may be present in the dataset. The navigation map may be based on an industry-specific businessworkflow and a next guided page may be based on the navigation map and arecord of what pages a user has previously viewed. In addition, themulti-source data collection includes integrating data from a pluralityof disparate data sources.

The methods and systems relating to a data page generation engine forpresenting a set of business activity guided pages of data fromindustry-specific data sources, wherein the pages include actionableelements and points of entry that may be determined based on an industryknowledge directed navigation map and that activate pathways to areas ofinterest based on a workflow for the business activity.

In an aspect of the methods and systems described herein, describedherein may be a method of generating a guided data page. The methodincludes receiving with a processor a user selection of an actionableelement that may be presented in a guided page on a touch screen userinterface. The actionable element may be associated with at least onecore dimension of a set of data, a portion of which may be depicted inthe guided page. The method further includes accessing with a processora navigation map for facilitating navigation among a set of the guidedpages that represent a plurality of related views of the data set basedon dimensions that may be present in the data set. The navigation mapmay be based on an industry-specific business workflow associated withthe data set. The method further includes generating a next guided pagebased on the navigation map, the user selection, a dimension associatedwith the user selection, and a record of what pages a user haspreviously viewed. The next guided page includes a table of data entriesderived from the data set.

The methods and systems relating to a content page generation engine forpresenting a set of business activity guided pages of content based onsources of data associated with the business activity. The pages mayinclude actionable elements and points of entry that may be determinedbased on an industry knowledge directed navigation map and that activatepathways to areas of interest based on a workflow for the businessactivity.

In an aspect of the methods and systems described herein, describedherein may be a method for generating a guided content page. The methodincludes receiving with a processor a user selection of an actionableelement that may be presented in a guided page on a touch screen userinterface. The actionable element may be associated with at least onecore dimension of a set of data, a portion of which may be depicted inthe guided page. The method further includes accessing with a processora navigation map for facilitating navigation among a set of the guidedpages that represent a plurality of related views of the data set basedon dimensions that may be present in the data set. The navigation mapmay be based on an industry-specific business workflow associated withthe data set. The method further includes generating a next guided pagebased on the navigation map, the user selection, a dimension associatedwith the user selection, and a record of what pages a user haspreviously viewed, wherein the next guided page includes a non-tabularcontent.

The methods and systems relating to a set of guided pages for operationof a business activity wherein the pages include row and column headingsthat represent data associated with at least one of industry-specificdata dimensions and domains related to a core data dimension. Each ofthe row heading, each column heading, and each intersecting cell may bea point of entry to a guided page that includes at least one of data andother content related to the point of entry.

In an aspect of the methods and systems described herein describedherein, may be a set of guided pages for operation of a businessactivity. Each of the sets includes a tabular arrangement of data valuesincluding row headings and column headings. The at least one of the rowheadings and the column headings represent data associated with at leastone of industry-specific data dimensions and data domains related to acore data dimension. Each set further includes a plurality of actionableelements disposed on at least one of a portion of the row headings, aportion of the column headings, and a portion of the plurality of datavalues disposed at each intersection of a row and column in the tabulararrangement. Each of the plurality of actionable elements enables accessto a guided page, in the set of guided pages, that includes at least oneof data and other content related to the row heading, column heading, ordata value on which the actionable element may be disposed.

In addition, the data dimension may be a core data dimension. The datadimension may be a core data dimension. The row and column headings mayrepresent data associated with the same data dimension. The row andcolumn headings may represent data associated with the same data domain.The column headings represent data associated with an industry-specificdata dimension and row headings represent data associated with a datadomain. The column headings represent data associated with a data domainand the row headings represent data associated with an industry-specificdata dimension.

The methods and systems relating to a user interface displaying tabulardata, wherein upon selecting a data item in the table, a user may bepresented with a new data domain and set of dimensions that have beenpre-determined by an analyst to be likely to be relevant to the userbased on the business context of the user and prior navigation steps ofthe user within the interface.

In an aspect of the methods and systems described herein describedherein may be a system including a data set including industry-specificdata sourced from a plurality of data sources used to operate a businessincluding business-internal and external data sources, the data setcharacterized by data having a plurality of industry-specific coredimensions, wherein at least one of the plurality of industry-specificcore dimensions may be common to at least two of the plurality of datadomains. The system further includes a user interface displaying a datatable that includes the dimensions that may be present in the data set,values that correspond to dimensions that may be common across theplurality of the data domains, and summaries that correspond to the coredimensions. In addition, upon selecting a data item in the table, a usermay be presented with a new data table representing a data domain and aset of dimensions that have been pre-determined by an analyst to belikely to be relevant to the user based on the business context of theuser and prior navigation steps of the user within the interface.

The methods and systems relating to a user interface displayinggraphical data, wherein upon selecting an item in the graph, a user maybe presented with a new data domain and set of dimensions that have beenpre-determined by an analyst to be likely to be relevant to the userbased on the business context of the user and prior navigation steps ofthe user within the interface.

In an aspect of the methods and systems described herein describedherein may be a system including a data set including industry-specificdata derived from a plurality of data sources used to operate a businessincluding business-internal and external data sources. The data set maybe characterized by data having a plurality of industry-specific coredimensions. The at least one of the plurality of industry-specific coredimensions may be common to at least two of the plurality of datadomains. The system may include a user interface displaying a data graphthat may be based on the dimensions that may be present in the data setthat may be common across the plurality of the data domains. The systemmay be configured such as upon selecting a data graph item, a user maybe presented with a new data graph representing a data domain and set ofdimensions that have been pre-determined by an analyst to be likely tobe relevant to the user based on the business context of the user andprior navigation steps of the user within the interface.

The methods and systems relating to a user interface displaying documentcontent, wherein upon selecting an actionable item in the userinterface, a user may be presented with document content from a new datadomain and set of dimensions that have been pre-determined by an analystto be likely to be relevant to the user based on the business context ofthe user and prior navigation steps of the user within the interface.

In an aspect of the methods and systems described herein describedherein may be a system including a data set including industry-specificdata derived from a plurality of data sources used to operate a businessincluding business-internal and external data sources. The data set maybe characterized by data having a plurality of industry-specific coredimensions. The at least one of the plurality of industry-specific coredimensions may be common to at least two of the plurality of datadomains. The system further includes a user interface displaying adocument content that may be based on the dimensions that may be presentin the data set that may be common across the plurality of the datadomains, wherein upon selecting a portion of the document content, auser may be presented with a new document content representing a datadomain and set of dimensions that have been pre-determined by an analystto be likely to be relevant to the user based on the business context ofthe user and prior navigation steps of the user within the interface.

The methods and systems relating to a tablet-based user interface havingdata that may be presented with associated dimensions, pre-defining anavigational transition from a first page to a receiving page. Thedimensions of the receiving page may be determined based on the dataselected by a user in the interface. The data on the receiving page maybe filtered based on prior actions of the user in the user interface.

In an aspect of the methods and systems described herein describedherein, may be a tablet-based user interface including a data setincluding data from a plurality of data domains, the data set may becharacterized by data having a plurality of industry-specific coredimensions. The at least one of the plurality of industry-specific coredimensions may be common to at least two of the plurality of datadomains. The tablet-based user interface may further include a pluralityof guided pages having data values associated with at least one of theplurality of data domains that may be presented with an associated coredimension in the tablet-based user interface. The user selection of adata value results in presenting a guided page in the tablet-based userinterface that has at least one dimension based on the selected datavalue and data values that may be filtered based on prior actions of theuser in the tablet-based user interface.

The methods and systems relating to a tablet-based user interface havinga graph that may be presented with associated dimensions, pre-defining anavigational transition from a first page to a receiving page. Thedimensions of the received page may be determined based on an itemselected by a user in the interface and wherein the data on thereceiving page may be filtered based on prior actions of the user in theuser interface.

In an aspect of the methods and systems described herein describedherein may be a tablet-based user interface including a data setincluding data from a plurality of data domains. The data set may becharacterized by data having a plurality of industry-specific coredimensions. The at least one of the plurality of industry-specific coredimensions may be common to at least two of the plurality of datadomains. The tablet-based user interface may further include a pluralityof guided pages having data graph items associated with at least one ofthe plurality of data domains that may be presented with an associatedcore dimension in the tablet-based user interface. The user selection ofa data graph item results in presenting a guided page in thetablet-based user interface that has at least one dimension based on theselected data graph item and data graph items that may be filtered basedon prior actions of the user in the tablet-based user interface.

The methods and systems relating to a tablet-based user interface havingcontent that may be presented with associated dimensions, pre-defining anavigational transition from a first page to a receiving page. Thedimensions of the received page may be determined based on an itemselected by a user in the interface. The data on the received page maybe filtered based on prior actions of the user in the user interface.

In an aspect of the methods and systems described herein describedherein may be a tablet-based user interface including a data setincluding data from a plurality of data domains, the data setcharacterized by data having a plurality of industry-specific coredimensions. The least one of the plurality of industry-specific coredimensions may be common to at least two of the plurality of datadomains. The tablet-based user interface further includes a plurality ofguided pages having content items including at least one of audiocontent, video content, and document content. The content may beassociated with at least one of the plurality of data domains that maybe presented with an associated core dimension in the tablet-based userinterface. The user selection of a content item results in presenting aguided page in the tablet-based user interface that has at least onedimension based on the selected content item and content items that maybe filtered based on prior actions of the user in the tablet-based userinterface.

The methods and systems relating to a large number of reports may becreated for a data set. In an example, the reports may represent a widerange of possible views of the data set based on dimensions of data thatmay be available for the data set that includes a range of data sourcesused by a business including internal and external data sources. In anexample, the reports may have tables that have dimensions that may becalculated or dynamic, and values or summaries that correspond to thosedimensions. In an example, the values may be related to the dimensionsto which they relate on the screen as presented in a particular report.Further, the values that may be related to the dimensions to many otherdimensions in the collection of reports that may be not shown. In anexample, a user can select a dimension or a value. In an example,selecting a presented dimension gives the user a chance to see a reportkeyed to the presented dimension that may include values and possiblyother dimensions that may be associated with the selected dimension. Inan example, selecting a value allows a user to see other dimensions thatmay have a relationship with the selected value along with other valuesthat may be associated with the other dimensions. In an example, apre-arranged guided navigation selects what set of new dimensions andvalues will be shown based on a prescribed flow.

The methods and systems relating to a large number of reports may becreated for a data set. In an example, the reports represent a widerange of possible views of the data set based on dimensions of data thatmay be present/available the data set that includes a range of datasources used by a business including internal and external data sources.In an example, the reports have graphs of values or summaries thatcorrespond to the dimensions, which may be calculated or dynamic. In anexample, values may be related to the dimensions to which they may begraphed on the screen as presented in a particular report. Further, thevalues may be related to the dimensions in the collection of reportsthat may be not shown. In an example, a user may select a dimension or agraphed value. In an example, selecting a presented dimension gives theuser the chance to see a report keyed to the presented dimension thatincludes graphs and possibly other dimensions that may be associatedwith the selected dimension. In an example, selecting a graphed valueallows a user to see other dimensions that have a relationship with theselection along with other graphed values that may be associated withthe other dimensions. In an example, pre-arranged, guided navigationselects what set of new dimensions and graphed values will be shownbased on a prescribed flow.

In an aspect of the methods and systems described herein describedherein may be a system including a data set including industry-specificdata derived from a plurality of data sources used to operate a businessincluding business-internal and external data sources. The systemfurther includes a plurality of graphs based on the dimensions that maybe present in the data set. In an example, the graphs may representvalues that correspond to dimensions that may be common across aplurality of the graphs and summaries that correspond to the coredimensions. The dimensions include core dimensions and dynamicdimensions. The system further includes a plurality of actionableelement graph items that includes actionable elements that may beselectable by accessing the graph items. In an example, the actionableelements may be associated with at least one of a dimension and a datadomain of the data set. In an example, selection of an actionableelement associated with a dimension results in automatic selection of anew graph from the plurality of graphs that may be associated with thedimension with which the selected actionable element may be associated.In an example, the selection of an actionable element associated with adata domain results in automatic selection of a new graph from theplurality of graphs that may be associated with the data domain withwhich the selected actionable element may be associated. The systemfurther includes a navigation map that facilitates determining whichnext graph to present in response to the selection of an actionableelement.

The methods and systems relating to a large number of content itemsassociated with a data set may be gathered and adapted for viewing in atablet user interface. In an example, the content items may beassociated with a wide range of possible views of the data set based ondimensions of data that may be available in the data set that includes arange of data sources used by a business including internal and externaldata sources. In an example, the content items may be further associatedwith values or summaries that correspond to the dimensions, which may becalculated or dynamic. In an example, the content items may be presentedwith a portion of the associated dimensions or values. In an example,the content items may be associated with many other dimensions that maybe not shown. In an example, a user may select a content item in theuser interface to see other content items, associated dimensions, orvalues associated with the selection. In an example, a pre-arranged,guided navigation selects what will be shown based on a prescribed flow.

In an aspect of the methods and systems described herein describedherein may be a system including a data set including industry-specificdata derived from a plurality of data sources used to operate a businessincluding business-internal and external data sources. The systemfurther includes a plurality of content pages including at least one ofaudio content, video content, and document content. In an example, thecontent pages may be based on the dimensions that may be present in thedata set. In an example, the content pages may represent values that maycorrespond to dimensions that may be common across a plurality of thecontent pages and summaries that correspond to the core dimensions. Inan example, the dimensions include core dimensions and dynamicdimensions. The system further includes a plurality of actionableelement content items that may includes actionable elements that may beselectable by accessing the content items, the actionable elementsassociated with at least one of a dimension and a data domain of thedata set. In an example, the selection of an actionable elementassociated with a dimension results in automatic selection of a newcontent page from the plurality of content pages that may be associatedwith the dimension with which the selected actionable element may beassociated. In an example, the selection of an actionable elementassociated with a data domain results in automatic selection of a newcontent page from the plurality of content pages that may be associatedwith the data domain with which the selected actionable element may beassociated. The system further includes a navigation map thatfacilitates determining which next content page to present in responseto the selection of an actionable element.

The methods and systems relating to a method of generating a set ofguided pages for operation of a business activity including gatheringdata from a wide range of data sources into a data repository;integrating data with a script-based domain editor. A script for thescript-based editor may be generated based on use of a visualintegration facility. The method includes determining dimensions of thedata, including determining dynamic dimensions. The method includesdetermining associations of data with dimensions. The method includescalculating one or more of data values and summaries for the dimensions.The method includes organizing the data into model-based datarepositories. The method includes configuring a set of business rulesfor accessing the model-based data repositories. The method includesgenerating a plurality of linkable pages that may be suitable forpresentation on a tablet computing device. The method includesdetermining permissible links among the plurality of linkable pages. Themethod includes associating particular links with actionable points ofentry in the guided pages to suit a workflow for a particular businessactivity.

In an aspect of the methods and systems described herein describedherein may be a method of generating a set of guided pages for operationof a business activity. The method includes gathering data from a widerange of data sources into a data repository. The method includesintegrating data with a script-based domain editor. In an example,script for the script-based editor may be generated based on use of avisual integration facility. The method includes determining dimensionsof the data, including determining dynamic dimensions. The methodincludes determining associations of data with dimensions. The methodincludes calculating one or more of data values and summaries for thedimensions. The method includes organizing the data into model-baseddata repositories. The method includes configuring a set of businessrules for accessing the model-based data repositories. The methodincludes generating a plurality of linkable pages that may be suitablefor presentation on a tablet computing device. The method includesdetermining permissible links among the plurality of linkable pages. Themethod includes associating particular links with actionable points ofentry in the guided pages to suit a workflow for a particular businessactivity.

In addition the step of determining dimensions of the data includesdetermining core dimensions. In an example, determining associations ofdata with dimensions includes determining associations of data with coredimensions. In an example, the linkable pages present data anddimensions in a tabular format. In an example, the linkable pagespresent graphical representations of data and dimensions in a graphicalformat. In an example, the linkable pages present document image contentassociated with the workflow. The method further includes furtherincludes calculating data summaries to facilitate guided page generationbased on a dynamic dimension.

The methods and systems relating to summarizing data into time periodbuckets for each of a plurality of core data dimensions that may bedetermined in a discovery and modeling process of data in a data set sothat a page generation engine serves time summarized pages for at leastone of the core data dimension without requiring access to the data set.

The methods and systems relating to a method of generating acolumnar-based data set of time-period based data summaries foroperation of a business activity including gathering data from a widerange of data sources into a data repository. The method furtherincludes integrating data with a script-based domain editor, wherein ascript for the script-based editor may be generated based on use of avisual integration facility. The method further includes determiningdimensions of the data, including determining dynamic dimensions. Themethod further includes determining associations of data withdimensions. The method further includes calculating one or more of datavalues and summaries for the dimensions, including time-period basedsummaries. The method further includes organizing the data intomodel-based data repositories. The method further includes configuring aset of business rules for accessing the model-based data repositories.

The methods and systems relating to the method above further includegenerating a plurality of linkable pages that may be suitable forpresentation on a tablet computing device. The method further includesdetermining permissible links among the plurality of linkable pages. Themethod further includes associating particular links with actionablepoints of entry in the guided pages to suit a workflow for a particularbusiness activity.

In an aspect of the methods and systems described herein described maybe a method of generating a columnar-based data set of time-period baseddata summaries for operation of a business activity. The method includesgathering data from a wide range of data sources into a data repository.The method further includes integrating data with a script-based domaineditor. A script for the script-based editor may be generated based onuse of a visual integration facility. The method further includesdetermining dimensions of the data, including determining dynamicdimensions. The method further includes determining associations of datawith dimensions. The method further includes calculating one or more ofdata values and summaries for the dimensions, including time-periodbased summaries. The method further includes organizing the data intomodel-based data repositories. The method further includes configuring aset of business rules for accessing the model-based data repositories.

In an aspect of the methods and systems described herein described maybe a method including generating a plurality of linkable pages that maybe suitable for presentation on a tablet computing device. The methodfurther includes determining permissible links among the plurality oflinkable pages. The method further includes associating particular linkswith actionable points of entry in the guided pages to suit a workflowfor a particular business activity.

The methods and systems relating to a guided page user interface thatenables presenting items of data from a first data domain, and inresponse to a user selection of a presented item of data, presentingitems of data from a second data domain based on the first and seconddata domains having a common core dimension.

The first and second data domains are distinct domains. The selecteditem of data is associated with the common core dimension. The commoncore dimension is determined during data discovery of a plurality ofdata sources representing the first and second data domains. The userinterface is adapted to access at least the first data domain and thesecond data domain.

The methods and systems relating to automated identification ofnavigation among pages across distinct sources of data and data domainsthat share a common core dimension and based on historical user actions.

In an aspect of the methods and systems described herein describedherein may be a method of automated update of a navigation map forfacilitating navigation among a plurality of guided pages. The methodincludes collecting transitions among guided pages representing aplurality of related views of a data set. In an example, the data setmay include data from a plurality of data domains and furthercharacterized by data having a plurality of industry-specific coredimensions. The at least one of the plurality of industry-specific coredimensions may be common to at least two of the plurality of datadomains. In an example, the plurality of guided pages may be based ondimensions that may be present in the data set. The method furtherincludes associating the transitions with the view of the data setrepresented by a source and a destination guided page, and with priortransitions performed by a user of the navigation map. The methodfurther includes determining differences between existing transitioninformation in the navigation map for the source and destination guidedpages. The method further includes updating the navigation map based onthe determined differences.

The methods and systems relating to dynamically navigating apresentation that includes a set of guided pages of content whereingroups of presentation pages may be linked to other groups ofpresentation pages through association with a set of core dimensionsthat may be determined during a data discovery process and that may becommon to the presentation pages.

In an aspect of the methods and systems described herein describedherein may be a method including using a processor to access a data setincluding data from a plurality of data domains and a plurality ofindustry-specific core dimensions. In an example, at least one of theplurality of industry-specific core dimensions may be common to at leasttwo of the plurality of data domains. The method further includesfacilitating dynamic navigation within a presentation including groupsof presentations pages that may be interlinked. In an example,facilitating dynamic navigation may be performed through userinteractions with displayable actionable elements, associated with theindustry-specific core dimensions that may be presented by the processorin a plurality of presentation pages on an interactive electronicdisplay. In an example, user interaction with one of the displayableactionable elements results in presenting a presentation page in acurrent group of presentation pages. In an example, user interactionwith one of the displayable actionable elements results in presenting aguided page of an alternate group of presentation pages that may beassociated with an industry-specific core dimension that may be commonto the current and alternate groups of presentation pages.

The methods and systems relating to automatically linking a plurality ofpresentations that may be organized around a set of common core datadimensions that may be extracted from disparate source data related tooperation of a business.

In an aspect of the methods and systems described herein describedherein may be a including using a processor to access a data setincluding data from a plurality of data domains and a plurality ofindustry-specific core dimensions. In an example, at least one of theplurality of industry-specific core dimensions may be common to at leasttwo of the plurality of data domains. The method further includesautomatically linking pages for navigation within a presentation basedon associations of the pages with the industry-specific core dimensions,the plurality of data domains, and the at least one core commondimension.

The methods and systems described herein may include automaticallylinking a plurality of presentations that are organized around a set ofcommon core data dimensions that are extracted from disparate sourcedata related to operation of a business.

BRIEF DESCRIPTION OF FIGURES

FIG. 1 depicts a functional diagram of guided page navigation.

FIG. 2 depicts an architectural diagram of guided page navigation.

FIG. 3 depicts a flow chart of guided page navigation.

FIG. 4 depicts a functional diagram of navigation among guided pages.

FIG. 5 depicts an architectural diagram of navigation among guidedpages.

FIG. 6 depicts a flow chart of navigation among guided pages.

FIG. 7 depicts a block diagram of navigation map based navigation amonga plurality of tables.

FIG. 8 depicts a flow chart of navigation map based navigation among aplurality of tables.

FIG. 9 depicts a flow chart of generating a time series object.

FIG. 10 depicts a functional diagram of navigation among a set ofdocuments.

FIG. 11 depicts an architectural diagram of navigation among a set ofdocuments.

FIG. 12 depicts a flow chart of navigation among a set of documents.

FIG. 13 depicts a flow chart of synchronizing guided pages between atablet and a sever.

FIG. 14 depicts a flow chart of column expansion.

FIGS. 15-18 depict guide navigation page features and a guided pagenavigation engine.

FIGS. 19-25 depict features of tablet based guided page generation,updating, and storage.

FIG. 26 depicts aspects related to a mobility module of a system forfacilitating data discovery for operating a business via guided pagenavigation.

FIG. 27 depicts an overview of core technologies described herein.

FIGS. 28-34 depict different page types available to the user that aredescribed herein and how they relate to each other.

FIG. 35 depicts exemplary data collection features.

FIG. 36 depicts an exemplary domain editor/data integrator output.

FIG. 37 depicts a domain editor interface.

FIG. 38 depicts a data integrator interface.

FIG. 39 depicts a visual integrator interface.

FIG. 40 depicts flow a data related to modeling described herein.

FIG. 41 depict exemplary application development features.

FIG. 42 depicts an exemplary production flow for updating guided pages.

FIGS. 43-52 depicts various aspects of dynamic presentation pathselection.

FIGS. 53-61 depict various presentations of indicators.

FIGS. 62-71 depict various column expansion capabilities and scenarios.

FIGS. 72-77 depict user interface capabilities related to capturing andassociating a photograph with data dimensions.

FIG. 78 depicts a block diagram of an embodiment of a columnar-baseddata processing system for generating guided pages;

FIG. 79 depicts a data flow diagram of an embodiment of the system ofFIG. 78;

FIG. 80 depicts a server cache lookup function of the data flow of FIG.79;

FIG. 81 depicts a block diagram of a columnar data set generated andemployed by the methods and systems described herein;

FIG. 82 depicts a block diagram of a deployment of the current methodsand systems;

FIG. 83 depicts a flow diagram for optimizing data queries;

FIG. 84 depicts an alternate view of optimizing data queries;

FIG. 85 depicts a c-plan for a fast time series computation;

FIG. 86 depicts relationships of select guided page types;

FIGS. 87 and 88 depict guided page table of content positioning;

FIGS. 89-91 depict multi-column capabilities described herein;

FIGS. 92-97 depict subset generation and handling capabilities describedherein; and

FIGS. 98-101 depict multi-view/filtered/multi-tab guided pages.

DETAILED DESCRIPTION

FIG. 1 depicts domain and data discovery and leaping among a pluralityof guided pages 102A, 102B, and 102C. Each guided page 102 may display adata structure containing domain specific data 104 associated with oneor more core dimensions 106. The core dimensions 106 may be industryspecific. Within a guided page 102 there are one or more displayactionable elements 110 and/or points of entry 112. Interacting with adisplay actionable element 110 in a referring guided page 102A resultsin the display of a new guided page 102B. The new guided page 102B maydisplay domain specific data 104 common with that of the referringguided page 102A but one or more of the core dimensions 106 may bedifferent from the core dimensions 106 on the referring guided page102A. Interacting with a point of entry 112 in a referring guided page102A also results in a new guided page 102C being displayed. The newguided page 102C may show domain specific data 104 associated with adomain which is different from that of the referring guided page 102A.However, at least one of the two industry-specific core dimensions 106may be in common with the industry-specific core dimensions 106 on thereferring guided page 102A.

FIG. 2 represents a data integration and display system for the creationof multi-domain data sets 206 that are used to build guided pages 102. Aplurality of data sources 202 are provided to and processed by theintegrator 204. The integrator 204 facilitates determining coredimensions 106 from the gathered data. Some of the core dimensions willbe industry specific. In addition, the integrator 204 facilitatesassociating the different domain specific data sets 104 with theidentified core dimensions 106 to determine industry-specific coredimensions 106. Of the plurality of core dimensions 106 identified, aportion will be common across two or more domain specific data sets 104.In addition, the integrator 204 may facilitate calculating time-basedsummaries of the gathered data associated with each industry-specificcore dimension 106. The source data and optionally the time-basedsummaries are organized into a plurality of model-based datarepositories 210 that feed the multi-domain data set 206. Themulti-domain data set 206, together with a page description and datastructure data 212 are used to facilitate creating the guided pages 102.

FIG. 3 depicts a flow chart for a method for guided page navigation. Themethod may include using a processor to access a data set comprisingdata from a plurality of data domains and a plurality ofindustry-specific core dimensions. The method may further facilitatedata discovery through user interactions with displayable actionableelements or points of entry. The method may further facilitatenavigation among the plurality of guided pages based on guided page highlevel description information accessible by the processor. The methodmay conclude with orienting information derived from the data in thedata set in an interactive electronic display based on guided page datastructure information that is accessible by the processor.

FIG. 4 represents a system for navigation among a set of guided pages102. Each guided page 102 displays a data structure containing domainspecific data 104 associated with one or more core dimensions 106.Within a guided page 102 there are displayed actionable elements 110.When a displayed actionable element 110 is selected the guided pagenavigation engine 404 identifies a next guided page 102 to display. Thenext guided page 102 displays a data structure where one or more of thecore dimensions 106 is common with that of the referring guided page 102but the domain of the domain specific data 104 may be different fromthat of the referring guided page 102. An industry expert develops rulesfor configuring the guided pages 102 and their inter-relationship basedon information that the expert determines to be pertinent to theoperation of a particular business activity. The navigation flow betweenguided pages 102 for different business activities may be impacted by aworkflow of a business activity 402 associated with source data used inthe guided pages, such as source data 202. The guided page navigationengine 404 uses the workflow of a business activity 402 together withthe referring guided page 102 to determine the next guided page 102 todisplay. In this way direct movement within and between different setsof domain specific data 104 is quickly accomplished.

FIG. 5 represents a data integration and display system for the creationof multi-domain data sets 206 that are used to build guided pages 102.The multi-domain data sets 206 are comprised of data from a plurality ofindustry specific data models 210, core dimensions 106, and domainspecific data sets 104. A plurality of data sources 202 are provided toand processed by the integrator 204. The integrator 204 facilitatesdetermining industry-specific core dimensions 106 from the gathereddata. In addition, the integrator 204 facilitates associating thedifferent domain specific data sets 104 with the identifiedindustry-specific core dimensions 106 to determine core dimensions 106that are common across more than one domain specific data set 104. Inaddition, the integrator 204 facilitates calculating time-basedsummaries of the gathered data associated with each industry-specificcore dimension 106. The source data 202 and optionally the time-basedsummaries are organized into a plurality of industry specificmodel-based data repositories 210 that feed the multi-domain data set206. The multi-domain data set 206, together with the workflow activity502 and the page description and data structure data 212 may be used tofacilitate creating the guided pages 102.

FIG. 6 depicts a flow chart of a method of navigation among a pluralityof guided pages. The method may use a processor to access a data setcomprising data from a plurality of data domains derived. The method mayfurther facilitate direct navigation among the plurality of data domainsin response to user interactions with displayable actionable elements orpoints of entry. The method may further facilitate navigation among theplurality of guided pages based on guided page high level descriptioninformation accessible by the processor. The method may conclude byorienting information derived from the data in the data set in aninteractive electronic display based on guided page data structureinformation that is accessible by the processor.

FIG. 7 shows a portion of the infrastructure supporting guided pages102, including a plurality of tables 702, a navigation map 706 and amulti-domain data set 206 with common core dimensions 106. The tables702 are comprised of one or more of core dimensions 106 that are presentin the data set 206, data values 704 that correspond to domain data sets104, and summaries 710. Within a table 702, a subset of the displayeddata values 704, as represented by the black squares, are actionableelement data values 110. The selection of one of the actionable elementdata values 110, results in the automatic display of a pre-selectedtable that includes at least one additional dimension 106 that isrelated to the selected actionable element data value 110 and datavalues 704 that are associated with the at least one other dimension106. An exemplary flow may be seen from Table D 702 showing Dimension Z106 where the data values 704 correspond to both Dimension X 106 andDimension Z 106. Upon the selection of an actionable element data value110 there was a transition to Table E showing Dimension X 106 and thedata values 704 are those associated with X, of which a sub set mightalso be associated with Dimension Z 106. Within a table 702 there mayalso be points of entry 112 associated with a dimension 106. Selectionof a point of entry 112 results in the display of a table that includesat least one of the data values 704 in the referring table and anotherdimension 104 that is related to the dimension associated with the pointof entry 112. This is illustrated in the flow from Table A 702 to TableB 702 or Table C 702. The data values 704 in Table B 702 and Table C 702correspond to Table A 702. The dimension 106 is either the same as TableA 702 or a dimension 106 related to the dimension 106 of the referringpoint of entry 112. Also shown in FIG. 7 is the navigation map 706. Thenavigation map 706 facilitates sequencing to the next table 702, guidedpage 102, new data values 704 or dimension 106 based on the currenttable 702, current dimension 106, current guided page 102 and theactionable element 110 or point of entry 112 selected.

FIG. 8 depicts a method flow chart of navigation map based navigationamong a plurality of tables. The method may access with a processor adata set comprising a plurality of industry-specific data models derivedfrom a plurality of data sources. The method may further generate withthe processor a plurality of guided pages representing a plurality ofrelated views of the data set. The method may further generate with aprocessor, for inclusion in a portion of the plurality of guided pages,a plurality of tables that comprise the dimensions that are present inthe data set. The method may further configure with the processor aplurality of actionable element data values that comprise actionableelements that are selectable by accessing the values. The method mayfurther generate with the processor a plurality of points of entrydimensions that comprise points of entry that are selectable byaccessing the dimensions. The method may conclude with referencing withthe processor a navigation map to determine which next guided page,table, values, and dimensions to select in response to the selection.

FIG. 9 depicts a method flow chart of generating a time series object.The method may gather data from a plurality of data sources includinginternal data sources associated with operating a business and externaldata sources; integrate the gathered data with a script-basedintegrator; determine core dimensions of the gathered data; associate aportion of the gathered data with core dimensions to identify commoncore dimensions for the portion of the gathered data; calculate one ormore time-based summaries of data associated with each core dimension;and the method may conclude by organizing the time-based summaries intoa plurality of distinct model-based data repositories.

FIG. 10 illustrates a system for rapidly accessing documents in atablet-based user interface where the documents are associated with atleast one core dimension 106 or data domain 104. Each guided page 102displays a data structure containing domain specific data 104 associatedwith core dimensions 106. The core dimensions 106 may be industryspecific. Within a guided page 102 there may be one or more displayactionable elements 110 associated with domain specific data 104 and oneor more points of entry 112 associated with core dimensions 106.Interacting with a display actionable element 110 results in accessing adocument link 1002 that facilitates presenting a document 1004 that isrelated to the same data domain 104 with which the actionable element110 is associated. Interacting with one of the displayed points of entry112 results in accessing a document link 1002 that facilitatespresenting a document 1008 that is related to an industry-specific coredimension 106 common with that which the point of entry 112 isassociated. The document presented may be any type of document, such asvideos, manuals, sell sheets, images of hard copy documents includingpurchase orders, shipping documents, product images, and the like.

FIG. 11 illustrates a data integration and display system for thecreations of multi-domain data sets and associating document links witha appropriate guided page 102. A plurality of data sources 202 areprovided to and processed by the integrator 204. The integrator 204facilitates determining core dimensions 106 from the gathered data. Someof the core dimensions may be industry specific. Of the plurality ofcore dimensions 106 identified, some portion may be common across two ormore domain specific data sets 104. In addition, the integrator 204facilitates associating the different domain specific data sets 104 withidentified core dimensions 106 to determine industry-specific coredimensions 106 that are common across more than one domain specific dataset 104. In addition, the integrator 204 facilitates calculatingtime-based summaries of the gathered data associated with eachindustry-specific core dimension 106. The source data and optionally thetime-based summaries are organized into a plurality of model-based datarepositories 210 associated with domain specific data sets 104. Themulti-domain data set 206, together with the page description and datastructure data 212 facilitate creating the guided pages 102.

FIG. 12 depicts a method flow chart of navigation among a set ofdocuments. The method may use a processor to access a data setcomprising data from a plurality of data domains, a plurality ofindustry-specific core dimensions; facilitate access to documents via aplurality of guided pages in a tablet-based user interface through userinteractions with displayable actionable elements or points of entry;facilitate navigation among the plurality of guided pages based onguided page high level description information accessible by theprocessor; and the method may conclude with orienting informationderived from the data in the data set in an interactive electronicdisplay based on guided page data structure information that isaccessible by the processor.

FIG. 13 depicts a method flow chart of synchronizing guided pagesbetween a tablet and a server. The method may access with a processor adata set comprising a plurality of industry-specific data models derivedfrom a plurality of data sources used to operate a business includingbusiness-internal and external data sources; generate with the processora plurality of guided pages representing a plurality of related views ofthe data set, wherein the plurality of guided pages are based ondimensions that are present in the data set; generate with a processor,for inclusion in a portion of the plurality of guided pages, a pluralityof tables that comprise the dimensions that are present in the data set;configure with the processor a plurality of actionable element datavalues that comprise actionable elements that are selectable byaccessing the values; generate with the processor a plurality of pointsof entry dimensions that comprise points of entry that are selectable byaccessing the dimensions; the method may conclude by referencing withthe processor a navigation map to determine which next guided page,table, values, and dimensions to select in response to the selection.

FIG. 14 depicts a method flow chart of column expansion. The method maygenerate with a server a plurality of guided pages for operation of abusiness activity; associate a cache tag with each page of the pluralityof guided pages; transmit the generated plurality of guided pages andthe associated cache tags to a client device during a firstsynchronization operation; update a portion of the plurality of guidedpages with the server and generating a set of updated cache tags for theupdated guided pages; receive cache tags from the client device thatindicate the guided pages that are present on the client device; comparethe received cache tags with the set of updated cache tags to determineupdated guided pages that are not present on the client; and the methodmay conclude with transmitting transmit the determined updated guidedpages and an updated cache tag for each of the determined updated guidedpages to the client during a second synchronization operation.

FIG. 15 depicts a table that includes a first column of core dimensionsgenerally as described herein. In the example of FIG. 15, the coredimensions may include brand, account, state, license type, distributor,sales person district, region, chain, supplier sales person, varietal,size, channel, etc. Although this table includes a first column of coredimensions and a first row of summary fields, it may not depict a guidedpage as described herein because it is intended to show how selecting acell in a guided page for each dimension listed in the first column(e.g. brand, account, etc.) may result in directing a user to a newguided page, such as to show data for different dimensions related tothe selected data. Further in FIG. 15 the column headings may be summaryfield labels, such as number of cases of current year to date (YTD),number of cases of YTD for last year (LYTD), number of accounts sold inYTD, number of accounts sold in LYTD, new accounts sold in YTD, accountslost in YTD, total new cases in YTD, total cases lost, sales velocity ofYTD, sales velocity of LYTD, case goals of YTD, number of accounts keptfor YTD, number of brands sold YTD, number of brands not sold YTD, etc.

Also, it is noted that selecting a cell, a row heading, or a columnheading will result in the methods and systems of guided page navigationpresenting a new page of data that is related to the item selected. Theinformation depicted in the intersecting cells indicates possibledimensions for presenting in the new page. A page generation engine maygenerate data for one of these possible dimensions based on contextualand other information. In particular, selecting the cell at theintersection of the “brand” row and the “# of Cases YTD” column mayresult in presenting a new guided page for an account, state, ordistributor dimension. Another example is selecting the cell at theintersection of the “account” row and the “# of Cases YTD” column mayresult in presenting a new guided page for one of dimensions, brands, orvarietals. It is noted that the possible destination dimensions for anygiven cell in a dimension row may depend on the column heading (e.g.what data is summarized). Therefore, although the possible destinationdimensions of the intersection of “brand” and “# of Cases YTD” includeaccount, state, and distributor, the destination dimension of theintersection of “brand” and “# of Accts sold YTD” is account.

FIG. 16 depicts one embodiment of how the selection of a new page asdescribed in FIG. 15 may be determined. A guided page generation enginemay include a guided page navigation engine 1602. A guided page 1610with underlying metadata is depicted for the core dimension “brand” andfor three summary columns as shown. The guided page navigation engine1602 may be operably coupled to a user interface in which the guidedpage 1610 is presented so as to capture user selections and metadataassociated with the guided page 1610. Additionally, the guided pagenavigation engine 1602 may be configured to receive information aboutthe role of the user, one or more workflows of the user, prior guidedpage selections (e.g. a history of this and any other guide page user),at least one core data dimension, business rule(s) that may be embodiedas a diveplan. The guided page navigation engine 1602 may analyze allavailable information, including the current guided page, currentdimension, current selected element on the guided page, metadata of thepage and cell, candidate next domains (e.g. taken from metadata for thecurrent page and/or selected cell), and the like to identify whichguided page should be generated or accessed for the user.

The guided page navigation engine 1602 may indicate the next dimension1608 to be used in the next page and a page generation engine maygenerate or provide a pointer to a page for the indicated dimension1608. The page navigation engine 1602 may facilitate filtering of thecandidate next dimensions based at least in part on prior actions of theuser in the user interface. In the embodiment of FIG. 16, such filteringmay result in only one of the three candidate dimensions (account,state, distributor) being selected.

FIG. 17 illustrates selection of a dimension for a next guided pagebased on input from a user selection in a guided page 1610. In thisembodiment, the cell selected by the user (intersection of “brand” and“# of Accounts Sold YTD”) may be defined to only offer one candidatenext dimension (accounts). Therefore, the guided page navigation engine1602 may be able to identify the next guided page dimension withoutrequiring analysis of information such as role, workflow, prior pages,core dimension, and the like. However, this information may be capturedand tracked for purposes of adjusting algorithms of a page generationengine.

FIG. 18 illustrates selection or identification of a next guided page1804 that may be retrieved from a cache of guided pages 1802. The cacheof guided pages 1802 may include a large number of reports for a dataset. The reports may represent a wide range of possible views of thedata set based on dimensions of data that may be present or available inthe data set. The reports may have one or more tables 408 that havedimensions that may be calculated or dynamic, and values or summariesthat may correspond to those dimensions. In the example of FIG. 18, thetable 1808 has a “brand” dimension and various year-to-date summaries.The guided page navigation engine 1602 may use cross-report dimensioninformation (e.g. that may be related to the table 1808) to facilitatedetermining what is the next guided page 1804 to be pulled from thecache of guided pages 1802.

Learning Techniques for Improving Guided Page Navigation

Existing technologies that facilitate user access to data models mayrequire the user to choose the “right” path so that the nextpresentation of data is a highly beneficial page of information. Incontrast, the inventive methods and systems of guided page navigationpresent only one screen for each user selection that is likely to be ofhigh relevance to the user. However, the screen that is presented inresponse to user selection may vary based on context and other factorsof the user environment. Therefore, it may be advantageous to useinformation about a user's actions once a new page is presented to helpa guided page navigation learning facility to learn which path is morelikely to provide highly beneficial information to a specific user. Byapplying learning techniques that incorporate user actions, the facilitymay allow the user to participate in determining the right path amongthe different paths.

In an unguided interface it is a challenge for the user is to choose theright path for achieving a particular point in a chain of differentpotential paths because there may be several paths that end up in aparticular point, yet some may require several interim steps that mayappear to disassociate the end result from the initial objective. In auser interface of guide pages, a guided page navigation facility maypresent data to the user to allow the user to navigate. In an example ofa guided page display of a matrix of data, a user may view the data ashaving greater significance from a row perspective than from a columnperspective and may rely on the row data to determine where to go next.Therefore, if selecting a cell in the matrix (which may be viewed by thenavigation facility as an equally weighted intersection of a row andcolumn) presents information that is not driven predominantly by the rowdata, the user may choose to go back (invoke a “back” function of theinterface) to the previous page. By collecting this information acrossmany users and many instances of use by each user, the user actions mayhelp to determine the next navigation steps for the user (e.g. forexample, new row, or column, or combination thereof). As noted above, auser action after having navigated to a particular page may beinformative of the perceived value of the particular page to the user ata point in the navigation chain.

Each type of user interface (e.g. an unguided interface or a guidedinterface) may be useful for learning how the user uses data discoverytools and therefore can be used to facilitate system learning forgenerating high relevance guided page navigation links. Each type ofinterface may be configured to track user actions and may pass them toone or more servers such that the system may reconfigure or refineguided page navigation based on those interactions. Learning mayleverage legacy products that may include a rich history of useractions, such as markers that are created and accessed by the user.

Learning may include automation based on learned data for pagegeneration or click action definition. Information about one or moreusers' activities in a free navigation product (e.g. for examplediveplan/DivePort) may be used to guide the selection of a preferredpath in the constrained (e.g. guided) navigation product. This type oftechnique may support automated identification of navigation among pagesacross distinct models and data domains.

In an example, if the user creates a marker or report in a legacyproduct (e.g. the unconstrained product), then the system may determinethe parameters and/or data items for which a page may need to begenerated. The system may be configured to use the pages generated bybusiness analysts as a guide or a library of pages and may use thenavigation of those markers as a guide to click actions or navigationamong those pages. In an aspect, the system may be configured to presentvisualization of a process of clicking on a cell to navigate to a guidedpage. The inputs used by a page generation engine may be based onindustry expertise such as dimensional information of the user, businessrules, user intervention to generate a page, and the like.

Navigation Across Distinct Data Domains

In an aspect, a system for facilitating data discovery for operating abusiness via guided page navigation across multiple models or datadomains is disclosed. In an aspect, the user may be on a dimension thatmay relate to more than models. For example, the salesperson appears inthe file or the model, which may also appear elsewhere. If the userclicks on sales person, the user may be navigated to a different datasource and different model. The system may be configured to allow theuser to hop from model to model or data source to data source.Therefore, the system may not be constrained by a mere “drill down”hierarchy within one of the models. The system may be configured toautomate navigation within a domain to a degree such as navigate to theother dimensions in that domain and the system may be configured tocreate those displays within that domain through that automation. Thisdomain information may be used as an input into the page generationengine.

Describing a Guided Page Tablet-Based User Interface

Aspects of methods and systems for converting data from data models asdescribed herein to guide pages for facilitating data discovery foroperating a business via guided page navigation is now described. In theinventive methods and systems described herein, a guided pagepreparation and navigation facility may determine inter-relationships ofdata (e.g. in the models) so that the facility ensures that users aredirected to a highly beneficial guided page each time a user selects aelement in a guided page. In this way, the facility leverages theserelationships to guide the user to stay within the boundaries of therelationships. Learning from user actions of current and legacy productsmay contribute to improving the guided page navigation actions.

In an embodiment, core technologies for accessing data models asdescribed herein may be configured to facilitate implementing a guidepage navigation environment. The core technologies may be configured tocreate an array of pages that may be available in a tablet portlet. Thepages may be created based on measures portlet that may be configured todefine the rows and columns. Core technologies such as a “marker” may beused to define parent dimensions, quick views, and things that the usermay want to vary (e.g. dimensions to vary), dimensions as columns,summary and information columns, a diveplan for merging multiple datamodels, and the like. Markers and similar definitions may act as aspecification for generating pages for the portlet. In addition, clickactions or menus that may be associated with points of entry in eachguided page may define the user navigation from one page to another.

Referring to FIG. 19, the tablet interface may include sections such asa column or tabs of “main menu”, “tablet documents”, and the like. Thesesections may describe top-level tabs in a DivePort. The sections mayinclude the information about the top-level menu items and may includelinks to information related to the dimensions. For example, the systemmay be configured to extract information out of a marker to definemeasures to generate pages for the tablet portlet. In this way the coretechnologies can generate a data repository of the summary andinformation columns that may be stored in the form of a set of javaarrays on the disk or java objects. Information columns may include weblinks, such as links to documents or web pages.

FIG. 20 depicts an example of a tablet matrix of cells that may bepresented in a guided navigation page user interface. The first row ofthe matrix may display an element of the dimension (e.g. region, salesperson, product, and customer). The cells below the dimension element ina particular column may represent details of the dimension element. Forthe region column region details North (N), South (S), East (E), andWest (W) are presented. In an example, some summary numbers are providedin the matrix (e.g. for example East: 5 sales people, selling 18products to 51 customers, and the like). If a user clicks on the number“24” cell, then the user's current position on the matrix will be at“products” and “South”. As this cell represents the product dimension(based on the column header), the next presented guided page will be a“product” page with information about the 24 products. If the number ofproduct is 24, the user may view 24 rows in the next guided page. In anexample, when the user clicks on the cell with the value of “24” such asshown in FIG. 20, the user may view a new screen that may be a list ofproducts with information (e.g. time, sales, volume) about each productfor the south region such as shown in FIG. 21.

In an aspect, the system may be configured to store a flat file in whicheach record is self-defining as described elsewhere herein. FIG. 22depicts an example of a portion of one entry in such a flat file. Thefile described herein may be configured to consolidate hierarchal orrelational database functionality through the flattening process toprovide information to the builder. The builder may be configured to usethe flat file to create a model. The file may be configured to includeinformation about the region, the salespersons, the products, and thenumber of customers. In an aspect, the information may be spread acrossmultiple files. Further, the file may be configured to includeinformation, such as “number of cases”, “dollars”, and the like. Asingle record in such as flat file may preserve complex links among dataitems. These links may include: the customer may be linked to asalesperson, which in turn may be linked to a region; the customer andproduct might be linked to an invoice (e.g. some value like dollars);the invoice described herein may contain things like date, dollar,volume, and the like of the product. Such information may be providedfrom one or more order entry systems, and the like.

FIG. 23 depicts an example of using the diver to facilitate generatingthe guided pages shown in FIGS. 20 and 21, for example. The modelcreated from the flat file by the builder may be opened in the diver.The diver described herein may be configured to use the model to createa marker. In an example, the user may dive into the region or may definesome columns such as for example “add in a dimensional count of salesperson”, “add in a dimensional count of product”, “add in dimensionalcount of customers”, and the like. The result may be a marker which is atext file with attributes, such as: region, define columns, dimensionalcount of salesperson, dimensional count of product, dimensional count ofcustomers, and the like.

Click actions may be defined in the DivePort to set up guided pagenavigation paths. A click action may have a type (e.g. for exampletablet page) and a destination, which may be a page ID of a unique page.The destination page may also inherit some variables that are passedfrom the initiating page (e.g. region). In addition, click actions couldpoint to web pages, documents, and other resources, including othertools and products described herein, including legacy general purposedata discovery tools such as DIVER and the like. Such functionality maybe enabled through a click action pointing to a back-end serverplatform. Authentication may also be provided through click actions tofacilitate access to secure resources. Click actions could also beparameterized based on aspects of a cell with which the click action isassociated (e.g. the cell or row/column heading) such as a coredimension, dynamic dimension, data domain, and the like.

In DivePort, the user may create a page such as by using “add page”objects. The user may create a new portlet such as a “measures” portlet.Once the user creates the new portlet, the system may user allow theuser to define and open up the marker. The order of operations insetting up successive guided pages that link to each other may include:defining the region page; defining the product detail page; going backto the region page and setting up the click actions. The click actionsmay include, for example, jumping from a product dimension count in theregion page to the product detail page so that when a user selects aproduct dimension count in a region page he is directed to a productdetail page.

While the above describes a user defining each page and the navigationguided paths among the pages, it is envisioned that a softwareapplication may be used to automatically generate the guided pages forthe user. Such an application may automate the design process forrelated pages by generating a collection of matrixes (e.g. by region, bysales person, by customers, by products, and the like) and thenautomatically generating all the possible paths. The learning techniquesdescribed herein, as well as industry-expert guidance may beincorporated into generating specific guided page links from all of thepossible paths.

Page generation and formation capabilities may be extended to the tabletso that information about rows and columns (e.g. for a column heading of“salesperson” a list of salespersons might be available on the tablet.Through association of one core dimension with a second core dimension,the software capabilities of the tablet may include generating guidedpages based on existing guided pages. In an example of tablet pagegeneration, presuming that branch sales data rolls up to a region'ssales data, a page of region sales data can be generated by aggregatingbranch sales data for all branches in a region. In an alternate example,a guided page that contains data for a plurality of dimensions, portionsof the page can be extracted to generate a page that is focused on onedimension.

FIG. 24 depicts data flow among several tools and features describedherein to facilitate generating guide pages for a guided page interface.An objective of the methods and systems described herein is to generallyallow the user to click on an item in a page and view the next pagewithout waiting for long page generation or download times. There may bea feature of the guided page user interface that enables updating thepages. When such a feature is activated for the first time, coretechnologies may be invoked to generate the pages for upload to thetablet device. Updating may include configuring a saved marker for eachpage, generating each page and caching the generated pages. Coretechnology components may include the DivePort for defining pages,DiveLine to support access to markers and the models, and the like. Theresult may be that the tablet or other user interface device may beconfigured to get the generated pages as data to be displayed.

In the example of FIG. 24, the tablet may connect to the DivePort, whichin turn may connect to the DiveLine (e.g. to bring in the markers andthe models). The DiveLine including the markers and the models may beconnected to a page generation and caching engine. The page generationengine may be configured to generate and cache data. The cached data mayinclude user-personalized information to present the user specific viewson the tablet. The system may be configured to allocate a time stamp anda unique number for each generated page. Using the example of region andproduct described for FIGS. 15 through 17, one product page per regionmay be generated (e.g. product_east, product_west, product_south, andproduct_north). These individual region-specific pages may includeamounts calculated on a region-specific basis.

Although the system may take time on the first update (e.g. 10 minutes,20 minutes, or the like) because the system may open up markers andmodels to generate the pages, the page generation and caching enginemaintains a list of cached pages that can significantly reduce recurringpage update times. As new data is processed through updates to thesource data (e.g. through a nightly production run), or as changes tothe page definitions are made in the backend (e.g. by a systems analyst)because the system is configured to cache the pages, it is possible todetermine which pages need to be generated based on the update and togenerate these new pages even before the pages need to be uploaded tothe tablet device. Further, the system may be configured to retrievepre-calculated information and deliver updates to the tablet. In anaspect, the system may be configured to regenerate the pages inanticipation of the tablet connecting up again based on the data presenton the system. The tablet may just need to download the pages and selectamong the pages based on the user requirement.

A collection of pages that may differ by one or more parameters (e.g.quickview variable) is indicated by the dotted lines in FIG. 24. In anexample, if the product is based on region and sub-region or region andsalesperson, then the system may be configured to generate pages byiterating through the combinations of the region and sales person.Therefore, such iterating may result in an exponential number of pagesbeing generated. Consequently, some computation may occur at the userinterface device (e.g. tablet) to select the pages, such as throughfilters or navigation. The tablet may use a select and pass technique(e.g. to send along “region” as the quickview parameter to define themarker such that the system may deliver the pages) for selecting a pagefor display from based on the quickview variable.

The page generation and caching engine may facilitate generation of alarge number of pages in advance of any need for the pages. However, toimprove responsiveness, the page generation and caching engine mayexecute a proprietary ETL language that allows for “last minute” ETLprocessing that is nonetheless extremely fast in generating a requiredpage or set of pages. One example is in the generation of tabs,cross-tabs, multi-tabs and the like. These are quite complex and couldinvolve a large number of pages. One solution is to embed the ETL forgenerating cross-tabs, multi-tabs and the like into the page generationand caching engine. As a result only common inherited variables need beprocessed ahead of time, thereby reducing the workload needed forcomplex solutions.

Although it is depicted in FIG. 24 that markers and models appear to beconnected to the page generation engine directly, it is as likely thatmarkers and models may go through DiveLine, DivePort, and the like toreach the page generation and caching engine.

FIG. 25 shows an example of guided page storage for facilitating datadiscovery for operating a business via guided page navigation. The datastorage on a tablet, server, laptop, and the like of guided pages andparameters may facilitate rapid and reliable access to guided pages.

The guided page storage may be configured to include a directorystructure and a data directory. A directory for data may be included inthe data directory for each page. In addition, information for sectionsmenus (e.g. tabs.info or sections.info) is stored for ready access. Eachpage may be configured to include page information (e.g. high leveldescription of the page) and page data structure (quickview values,selections array such as the page data array with things like: East=1;south=2 such that the system may find the right page within the datapages). The system may be configured to include the cache informationand other page information such as data pages 0.data (data for a pagethat you would see for that selection), 1.data, 2.data, and the like asshown in FIG. 24.

The cache information described herein may be useful for determining thepages that need to be updated such as when the guided pages are updatedor when the tablet is connected to the Internet. Each page may beassociated with a cache token that may be exchanged between a userinterface device (e.g. a table) and a server to keep track of the pageson the tablet or other user interface device. The tokens may then beaccessed when new data has been produced into the data models todetermine which pages are impacted. Alternatively, a timestamp may begenerated for each page so that pages generated before a particularupdate may be identified for being updated. In either case, a uniquenumber may be created and associated with a particular view of the datato facilitate managing cached data to ensure accuracy, timely updates,and the like. Cache tokens may be attached to presentations, images,data tables, files, documents, and configuration information and otherobjects handled by the methods and systems disclosed herein. During asynchronize operation, the client and the server can send each othercache tokens to describe what data is available on each side. If theclient is missing certain data that it or the server decides it needs tohave, the server may send the client the data and the cache token thatidentifies it. The cache tokens can be used to identify staleinformation that needs to be updated during a synchronize operation. Thecache tokens can also be used to identify fresh information that needsto be transferred during a synchronize operation, such as, for example,information entered by a user on a client device, photos or videos takenby a client device, or the like.

The system may include the ability to synchronize data on the serverwith data on the client. This allows data to be stored locally, andavailable even when the client is not connected on the network. Bystoring it locally, the data can be displayed quickly.

Guided Page Navigation

As described herein, a system for facilitating data discovery foroperating a business via guided page navigation among and betweendimensions and domains of data for operating the business may be used tofacilitate gathering data from various sources. The source dataingestion or data collection may include data from customer data sourcesand external data sources. The customer data sources may include flatfiles, spreadsheets, data warehouse, SQL databases, variously formattedor sourced data, legacy systems, transactional systems, and EnterpriseResource Planning (ERP) systems. The external sources may include thirdparty feeds, market or demo data. Data collection and ingestion aredescribed elsewhere herein at least in regards to FIG. 35.

In addition, the system for facilitating data discovery for operating abusiness via guided page navigation may facilitate data integrationusing various tools and facilities of the system including a domaineditor. Integrator scripts may enable the data integration that is partassociated with the domain editor. A visual data integrator tool thatmay facilitate designing data integrator scripts. The integrator scriptsmay include various business rules, calculations, filtering, and thelike that may be applied to the data. The output may be a de-normalizeddataset that may be described as comprising a set of single records thatare self-defining. The domain editor tool set may facilitate discoveringdata in the sources (e.g. in an SQL database). The domain editor mayalso facilitate extracting data from SQL and other databases, amongother things.

An industry expert may typically explore source data associated withoperating a business with the use of the domain editor to figure outwhat information may be available and tag or define the data during oneor more exploration steps. This may involve defining data types/fields(e.g. “core data dimensions”), which may be the most important types ofdata for operating the business. An industry expert may ask questionsabout operating a business to help determine the core data dimensions.As noted above, the domain editor may include a visual data integratortool for designing scripts for the data integrator.

The domain editor data integrator script engine uses a novel scriptbased language. The script-based data integrator is configured togenerate denormalized data sets that results in highly efficient“joining” of data from various types such as supplier data, productrelated data, sales person related data, and the like. The result isalso accomplished at a much faster rate with the use of the dataintegrator than may be done with or in a relational database (RDB).

As large source data sets may be required to be processed quitefrequently (e.g. nightly production runs) to have updated data availablefor users daily, the time and computing power required to process thedata may require careful planning and management. One way that this maybe done is that an industry-expert may provide guidance to the tools ofsuch a production system. The expert may create business rules that canbe used during the nightly production model update process to seek toachieve a balance between pre-calculated summaries (that may requirecomputation during production) and raw data generation (that may requireprocessing in near-real time in response to user selections).

The expert may need to tradeoff computing data during the production runversus having to compute data in near real-time when the user selects adata element which produces a new guided page of data. Also of importantconsideration, if production is not complete in the time allotted for agiven production run, then the data from that production run may not beavailable to be used the next day. In addition, if production isadjusted so that it leaves much of the most popular data innon-summarized form, a tablet user interface may operate too slowly.Therefore, a tradeoff between computation during the production run andcomputation needed in real-time may be required so as to maintain anequitable balance. In general, source data may be organized intodimensions, summary fields, information fields, and the like.

Dimensions of Data

The dimensions of data associated with the data models described hereinmay be classified into core dimensions and dynamic dimensions. Thenumber of core dimensions may be limited to 32 because as the number ofcore dimension increases, the processing time that may be required fordata discovery may increase exponentially. This is due to a process thatinvolves calculating summaries of all relevant data from all other coredimensions for each core dimension.

In some cases, large databases may be limited to 12 or fewer coredimensions to ensure that data will be completely processed in apredefined time period or a specific time period such as overnight. Forexample, the processing of the data may be complete between 1 AM to 6AM, thereby enabling users to get updated data in the morning.

The dynamic dimensions may be significant because they may not increaseproduction processing time while allowing for views of data from adimension perspective in a way that may be similar to how data for coredimensions may be viewed. In some cases, the dynamic dimensions may beviewed by requiring some additional processing at the front end when theuser wants to view the data.

Another type of dimension is a central dimension. A central dimensiondoes not directly relate to the organization of data for data modelingas described above. Rather, a central dimension is a user-centricconcept that descriptively represents a higher level or ‘gathering’ ofdata into an organization that is familiar to a user. Generally the term“central dimension” or “central data dimension” used herein refers to alogical grouping of data, such as a plurality of core dimensions, thatcan be represented to a user as a dimension around which other dimensionand data domains can be clustered. This may be presented to the user asa high-level tab or high-level entry in a hierarchy, although other suchrepresentations are possible. Therefore, a central dimension may be acore dimension; however a central dimension may represent a logicalgrouping of more than one core dimension. Also, a central dimension mayindicate a logical grouping of data that represents a subset of dataassociated with a core dimension.

Summary Fields

Also of important consideration are the summary fields that may betagged as fields that may be summarized or fields that may include datathat may be put into a summarized format for rapid access during guidedpage generation.

Information Fields

Information fields are also important considerations at least becausedata in information fields may be non-summarizable. Information fieldexamples include customer attributes, such as customer phonenumber/information, category, region, and other similar informationrelated to a customer.

The system for facilitating data discovery for operating a business viaguided page navigation may facilitate using various tools and facilitiesof the system to identify and organize source data into distinct datadomains. The organization of data into these distinct data domains maybe a part of the model building process that may result in distinct datamodels for each distinct data domain. In particular, a separate datamodel may be built by for each distinct data domain. Therefore, theremay be more than one model, which may help improve access speed withinand across the data domains.

The system for facilitating data discovery for operating a business viaguided page navigation may facilitate creating and applying businessrules to the models. Tools such as diver, ProDiver, and the like toaccess or view the data domain models may use the business rules. Accessto the distinct data domains may be further guided by systemcapabilities including markers, diveplans, and the like. Dive plans maybe used to save a business-rules-based view of the models and in generalhave been described elsewhere herein at least in regards to FIG. 41. Amarker may be used to attach rules to the data and to save intermediateresults and has been described elsewhere herein with reference to FIGS.27, 40, and 41.

The system for facilitating data discovery for operating a business viaguided page navigation may include a tablet application (app). Thesystem may include DivePort functionality described herein at least withreference to FIG. 41 to help with the delivery of distinct domain datafrom the data models to a tablet page generation subsystem. Inparticular a tablet page generation subsystem may include or connectwith a page generation engine may be used to generate/adjust pagessuitable for tablet or other touch-screen device presentation. Inaddition to page generation, a page generation subsystem may considerand factor in location of data for generation of the pages, such astablet storage versus server storage of data Has the data for generationof the tablet-compatible page been cached (e.g. in the tablet device) toallow off-line page generation and navigation?

Navigation within and among the distinct data domains may be impacted byindustry expert knowledge, such as in the form of various configurationsof the system including dimensions, summary fields, page points ofentry, pathways to areas of interest from those points of entry,selection of data types to be presented in guided pages, and the like.

The system for facilitating data discovery for operating a business viaguided page navigation may include solutions for various industriesincluding, without exception: healthcare-specific apps for various rolesin healthcare for example nurse data, intake data, ER tech, and thelike; winery-specific apps for suppliers, distributors, retailers, andthe like; distribution-specific apps for products such as liquor, wine,automotive parts, financial services, or any other product or service.

The system for facilitating data discovery for operating a business viaguided page navigation may provide benefits through tablet-baseddashboards that may facilitate visibility into key business processesthereby replacing “gut-feel” decisions with “fact-based” decisions thatcan be based on ‘one version of the truth’ rather than on each user'sown spreadsheet view. Additionally it may facilitate greater timelinessand accuracy in aligning business activity with company goals/strategy.

A system for facilitating data discovery for operating a business viaguided page navigation may further provide the benefit of navigatingthrough distinct data domains that are clustered around and acrosscentral dimensions in a way that may give the sense of being intuitiveto a user. The user may perceive the possession of an ability to‘freely’ navigate about the data, even though each selectable cell orrow or column heading may lead the user to another contextually relevantguided page.

In addition to accessing data and summaries, the methods and systemsdescribed herein may also provide access to documentation, videos,manuals, sell sheets, images of hard copy documents including purchaseorders, shipping documents, images of product, and the like usingsimilar guided navigation techniques. Therefore, access to documentationmay be handled logically in a very similar way to assess to the datadescribed herein. In an example, the documentation may be linkeddocument by document to the data, and in particular to the core datadimensions so that each document may be accessed in a proper businesscontext.

For example, if a user reviews sales figures for a specific brand ofproduct such as a wine, the user may have access to invoices for thesales of that wine. The user may also have access to images of the winebottle label, a marketing sheet provided by the wine maker, and thelike. In this context, the user may not likely have direct access to theinvoices for product variants such as scotch or other different productsbecause these documents would not be contextually relevant to the salesfigures of the specific brand of the wine. Therefore, the system forfacilitating data discovery for operating a business via guided pagenavigation may be used by an organization where document accessrequirements may be different for different personnel depending uponrole business objective, project, requirement, and the like. The systemfor facilitating data discovery for operating a business via guided pagenavigation may be configured so that a single application may be able tocater to different access needs of different users. Access of differentusers may be limited by a user's authentication level.

The system described herein may include a complete document managementsystem that may be organized in a variety of ways. In addition, thecomplete document management system may be organized by industry forexample wine industry, liquor industry, automotive parts industry,healthcare industry, and the like. The complete document managementsystem may be organized by activities or roles of a specific user forexample, sales, support, planning, regulatory activities, and the like.The complete document management system may be organized based oncompany specific requirements for example, business plans, goals,procedures, contacts, invoices. For example, The requirements may useris interest at selecting bill data, orders or user interest in orderdata, product images or labels or user

Sum- Sum- Sum- Dimension Summary 1 Summary 2 mary 3 mary4 mary 5 Dim ValSummary 1 (domain 1) of domain 1 Domain 2 Summary 2 of domain 2 Domain 3Summary 1 of domain 3interest in selecting product data. The complete document managementsystem may be organized based on product placement such as store images,signage, floor plan and the like.

The system for facilitating data discovery for operating a business viaguided page navigation may be configured so that the system may link upaccess permissions to other contextually relevant data and automaticallygrant access to the data. The contextual relevancy may be differentbased on industry requirements and may be configured by an expert orcontextual relevancy using markers, diveplans, and the like.

In an example where the tablet based version of the system is used, auser interface may be included through which the user may select anyelement in a matrix (including a row or heading column) and the nextscreen may appear as a particular destination that will include a viewof data that is related to the selected element. Such an example isdescribed below.

A user may choose a dimension to present in a left column (row headings)and the user may have an option to switch the choice to a differentdimension. Each dimension value (in the left column) may be selected toview details of that dimension (e.g. selecting a “sales person”dimension may present a list of sales persons in a “sales person”column). Column headers may represent summary fields, time ranges, andthe like as described above. The values in each cell may represent asummary of the selected data (e.g. sales) for each entry (dimensionvalue) shown in the left hand column (row headers). The cells in theexample below may be filled with summary data values.

The tablet solution may allow selection of a cell value which points toa data structure that defines the next table of data to display. Thisnext table may be related to the row/column heading of the selected celland may be filtered thereby to present the subset of the type of page.The industry expertise/knowledge/guidance as stated above may be appliedin determining summaries to be present along the column headings and newsummaries to be present when a cell is selected, thereby circumventing aneed for presenting menus or a selection of destinations when a value isselected. The industry expertise may be used to generate the summariesand to determine the summaries and new dimensions to be presented in thenext page view.

In a further example, an executive may want to start his day with “news”about details of a business. The news may include a new product, newpresentation, or the like information. The executive may want to viewand download these new items. The executive may want to look at data(e.g. in a tabular view) to navigate among a set of guided pages thatprovide key high-level summary data. The executive may want to get alist of things to do (the list maybe driven by the data), or people tosee (again driven by the data). The executive may want to be able toenter new information (e.g. a contact note). This function may becarried out by a single tablet-based application of the system forfacilitating data discovery for operating a business via guided pagenavigation.

Workflow may be an important aspect of the information that is displayedto the user in each successive screen. As may be noted in the executiveexample above, the executive's workflow at the start of a day mayinvolve several non-data items that are mostly driven off of the data.For example, if the executive views sales data is considered and itshows an unexpectedly sharp change, the executive may want to schedule aconference with the director in charge of the unit that experienced thechange. If the summary data indicates that the quarterly sales goals maybe exceeded, the executive may want to discuss sales goals for the nextquarter. Similarly, various other scenarios may be possible.

Therefore, although guided page navigation may be keying off of thetechnology and the expertise off of the industry, such navigation mayalso need to incorporate and key off of the workflow. For example, itmay consider what the users may need to do in order to carry out theirdaily activities and may combine the results in a set of guided pages.

The system and methods described herein for facilitating data discoveryfor operating a business via guided page navigation may be useful in avariety of applications including product management or operations, ordistribution or retailing such as for example winery management,hospital operations, wine or liquor distributor, retail store,monitoring sales progress or for promotional activities, presentationsand the like.

Exemplary markets or environment may include wineries, healthcare,liquor distribution, automotive parts, financial services, and the like.Some of the exemplary deployment scenarios in various exemplaryenvironments are presented below.

In an exemplary scenario, sales representatives may visit to a retailstore. The system for facilitating data discovery for operating abusiness via guided page navigation may be used by the salesrepresentatives for monitoring sales progress or for promotionalactivities, and the like in a retail store. For example, the salesrepresentatives may track down available inventory at a customer requestfor a particular commodity or may fetch discount or promotional offerdata for customers using the application.

In an exemplary scenario, suppliers may analyze sales data. The systemfor facilitating data discovery for operating a business via guided pagenavigation may be used by the suppliers or sales experts of anorganization for tracking and monitoring sales data. This may be usefulin checking company progress and may serve as an indicator that may beused in sales forecasting, inventory management, production forecastingand the like activities. The tablet may be configured to be linked tocompany forecasting data that may get updated on its own based on datafed into the system for facilitating data discovery for operating abusiness via guided page navigation.

An exemplary scenario may include operating a professional servicesbusiness. In such cases, the system for facilitating data discovery foroperating a business via guided page navigation may be used by such aslaw firms, consulting firms for project management, sales activity, teamwork sharing, and the like.

In an example, a data analyst may use the system for facilitating datadiscovery for operating a business via guided page navigation inextracting reports, uploading results and may fetch related datacontextually relevant to a single record thereby increasing efficiencyand reducing time required for analysis.

FIG. 31 depict aspects related to mobility module of the system forfacilitating data discovery for operating a business via guided pagenavigation. The system may be configured to allow the user to access themodel data from anywhere (e.g. via the diver). A native tablet-typedevice application may be used to allow the user such access fromanywhere by including some data (e.g. a canister model) on the tabletdevice. In an example, the mobility module may leverage core diverinfrastructure to access the system from anywhere. The system may allowoff-line (disconnected) use to the user. For example, the user mayaccess the data off-line without getting connected to the network. Themobility module may allow access to the critical Key PerformanceIndicators (KPIs) from anywhere or anytime. A DivePort facility mayfacilitate easy design of the mobile views and then publish views formobile devices such as smart phone, tablets, and the like.

In an example, NetDiver module of a system may be used to facilitatedata discovery for operating a business via guided page navigation. TheNetDiver module may provide browser-based zero-footprint ad hocanalytics interface. The NetDiver module described herein is providedfor general and advanced users. The NetDiver module may be configured toallow access and analyze data via the Web. The NetDiver module mayreside on a Web app server and may act as a bridge between user's webbrowser and DI models. The NetDiver module enables a user to dive,focus, summarize, create calculators, save work, and the like.

Further to an example depicted in FIG. 31, budget, planning, andforecasting module of the system may be provided for facilitating datadiscovery for operating a business via guided page navigation. Thebudget, planning, and forecasting module may include head planner leads,collaborative effort aggregating user's own slices of the data. Thebudget, planning, and forecasting module may include flexible toolkitapproach such as to expect a custom planning development cycle to meetbusiness process requests. The budget, planning, and forecasting modulemay include an integrated solution that sources final plan through diverwhile using DiveLine for access control and role-based security. Thebudget, planning, and forecasting module may include planner uses of acomponent interface (e.g. Console, Toolbar) to edit the administrationcapabilities.

In an example, a cell diver module of the system may be provided forfacilitating data discovery for operating a business via guided pagenavigation. The cell diver module may act such as an add-in to an MSExcel® file. The cell diver module may allow diving from MS Excel® basedconsole. The cell diver module may pull data directly from DiveLine intoan Excel file. The cell diver module may allow opening models, markers,DivePlans, Tunnel files, and the like. The cell diver module may allowto dive further into data and apply MS Excel® based functions.

The ProDiver or diver module of the system may facilitate data discoveryfor operating a business via guided page navigation.

In an example, various aspects related to technology components such asDivePort of the system may facilitate data discovery for operating abusiness via guided page navigation.

In an example, dashboards module of the system may be provided forfacilitating data discovery for operating a business via guided pagenavigation.

In an example, a scorecard module of the system may be provided forfacilitating data discovery for operating a business via guided pagenavigation.

In an example, an indicator module of the system may be provided forfacilitating data discovery for operating a business via guided pagenavigation.

In an example, a compound indicator module of the system may be providedfor facilitating data discovery for operating a business via guided pagenavigation.

In an example, a measure module of the system may be provided forfacilitating data discovery for operating a business via guided pagenavigation.

In an example, maps module of the system may be provided forfacilitating data discovery for operating a business via guided pagenavigation.

In an example, a production module of the system may be provided forfacilitating data discovery for operating a business via guided pagenavigation.

Overview Figure

FIG. 27 describes structural components of a system for facilitatingdata discovery for operating a business via guided page navigation. Thecomponents presented in FIG. 27 may provide functionality related todata ingestion, integration, summarization, analysis, access, and userlevel presentation. These components may be described herein and mayinclude Builder, Diver, Marker, Data Integrator, DiveLine, ProDiver,WebDiver, Report Diver, Model Splitter (Personalized Models), DetailBuilder (Detail Row Access), DI-Broadcast and Analyst (DeliveryAutomation), Dimensional Insight Access Language (DIAL), Diveport,htmlDiver, Tunnel such as to access back to a data warehouse, PageGeneration Engine, Canister Models, and the like.

Page Types that Support Information Leaping

Page types that may facilitate information leaping in presentation typeenvironments may include access pages, central pages, overview page,list pages, and others. Page types are depicted in FIGS. 28 through 34.FIG. 28 depicts different page types. FIG. 29 depicts different pagetypes that facilitate leaping and how they relate to each other. FIGS.30-33 depict examples of different page types for various industries.FIG. 34 depicts a central page for a “State” dimension.

Access pages may alternatively be referred to as “entry” pages that maybe the first page or first few pages to be displayed in response to auser selecting a menu item. While access pages may include selectabledata items (for guided page navigation) access through the selectabledata items may be done via a “central page” that is described hereinafter. Access pages may provide basic viewing capabilities includingsorting, and the like. Each menu item may be linked to an access pageand additional access pages may be linked through selectable items on anaccess page. Each central dimension of the dataset that is beingexplored through the information leaping capabilities described hereinmay be accessed via one or more access pages. Access pages mayfacilitate access to other page types such as central pages, list pages,and overview pages. In an example, an access page may provide directaccess to a central page type and/or to an overview page type andindirect access to a list page type as depicted in FIG. 29.

A second page type is a central page that may be associated with acentral dimension as described herein. Generally a central page isaccessed through an access page when a central dimension is selected onthe access page. In a dataset about U.S. states, the data item “state”would be a central dimension. Therefore, clicking on a state name (e.g.Georgia) or the like in an access page may leap to a central page basedon the state central dimension value Georgia. Within the Georgia centralpage attributes about the item may be displayed (number of hospitals,demographics, health care data, infant mortality, education, etc.). Byselecting attribute item, such as hospitals on the Georgia central page,a new central page for the central dimension Hospitals might bepresented. An exemplary central page is depicted in FIG. 30. Centralpages may facilitate access to other page types, such as access, list,and overview page types. In an example depicted in FIG. 29, a centralpage may provide direct access to access type pages and list type pageswhile providing indirect access to overview type pages.

A third page type is a list page that may show details of a selecteditem. In an example, selecting a count of hospitals in the Georgiacentral page might leap to information about hospitals and may result ina list of all of the hospitals in the state of Georgia. List pages mayprovide access to other pages, such as access, central and overview pagetypes. In an example depicted in FIG. 29, a list page may provide directaccess to central and overview page types while providing indirectaccess to access type pages.

A fourth page type is an overview page that may be used to combinetop-level information from different business areas to provide anoverview and entry to other business information. An overview page maybe accessed from a main menu. An overview type page may facilitateaccess to other page types, such as access type pages, list type pages,and central type pages. In an example depicted in FIG. 29, an overviewpage type may provide direct access to a list page type and an accesspage time while providing indirect access to a central page type.

An Example of Information Leaping

In an example of information leaping, a list page of states andhospitals per state might be displayed. Selecting a count of hospitalsin a specific state (e.g. select 95 next to Alabama) may display the 95hospital names along with various data items for each hospital. One suchvarious data item might be in the category of gross patient revenue.Selecting this item might provide sort options for the entries in thisitem (sort the list of hospital names by gross patient revenue). Theresult is an ordered list of the 95 hospital names and other data (e.g.location) based on gross patient revenue. Selecting a location (e.g.city name) from this list may result in an information leap fromhospital specific data to location specific data. This changed datadomain may show a count of clinical services available in the selectedlocation. By selecting this count, a listing of the clinical servicesmay be presented along with a count of hospitals that offer eachclinical service. The methods and systems described herein that enableinformation leaping may facilitate such leaping from state to hospitalto location to clinical services, and the like with a minimum ofselections.

Data Sources

FIG. 35 depicts aspects related to data collection and a data collectionmodule of a system for facilitating data discovery for operating abusiness via guided page navigation. Data collection may include datasources such as for example, but not limited to, legacy systems, ERPsystems, spreadsheets, data warehouses, suppliers, distributors,government such as local, state, federal, and the like such as shown inFIG. 35. Data collection may further consider and support various typesof information such as product information, state laws and regulations,federal laws and regulations, local laws and regulations, customerinformation, purchase history, news, industry guidelines, businessplans, customer information such as name, address, city, zip, consumesliquor on or off premises, purchase history, and the like.

The data sources may further include product information. The productinformation may be obtained from any of the data sources, such as asupplier or distributor product database that may include theinformation about a distributor of a product. Suppliers constantlydevelop new product and distributors constantly update their supply ofproducts. Therefore, the product information may be obtained from one ormore feeds of a supplier (e.g. creator of the product), distributor, orthe like. The product information may for example include UPC codes, PANnumber of the supplier, alcohol content, color, sell sheets, videos,marketing materials, and the like. Therefore, solutions described hereinfor facilitating data discovery for operating a business via guided pagenavigation may address diverse and detailed product information.

The sales chain of liquor and wine is complex primarily due to complyingwith regulations for distribution of liquor, beer, and wine. Forexample, a winery or distillery typically sells to a supplier who mayaggregate products from a wide range of small wineries. The supplierprimarily sells to a distributor, who sells to a retailer who furthersells to a retail consumer. Solutions for data discovery for operating abusiness via guided page navigation may include this tiered system.Therefore, solutions for data discovery for operating a business viaguided page navigation for suppliers may focus on different data(different dimensions of the data) than solutions for distributors,retailers, and the like.

The data sources may further include supplier information. For example,the winery or distillery may be a supplier or can use a second partsupplier to sell its products. Using the example above such a suppliersells to a distributor; the distributor sells to a retailer; theretailer sells to a customer who may be a restaurant (on-site) or aconsumer (take home). Therefore, the supplier information or datadescribed herein may include data from the winery and/or from the secondpart supplier. As noted above, this information may include productattributes (such as for example alcohol content of each product, color,and the like). However, it may also include sell sheets (marketable,positioning of the products, videos, differentiators from other productsor competitors, displays, incentive items, and the like) and the like.The supplier information may further include vintage, beer shelf life,and the like. The supplier information described herein may furtherinclude cost of the product (such as deals volume based business, tieredpricing, and the like). For example, if a product or set of products islagging in sales, a supplier may provide compensation to the distributorfor each product that is distributed over a period of time. The supplierinformation may further include discount participation for sale to thecustomers that may be volume based (from the customer's perspective) ormay be based on a pure discount level. The supplier information mayfurther include product sales history, supplier sales history, and thelike. In an example, industry consolidation results in distributors orsuppliers merging or otherwise changing in a variety of ways includingchanging distributors. Such changes may impact a customer of a supplieror distributor so the supplier may provide information that helps thecustomer in gauging the business for a new supplier to set expectations.In some situations, sales may be tracked and provided as sales historyfor a new supplier. The suppliers may target or fix goals across most ofthe products for sales over a time frame (goals or quotas) that may bedistributed down to the sales person level. Therefore, solutions fordata discovery for operating a business via guided page navigation maysupport functionality such as to identify if a sales person meets thesequotas or goals.

Further, the supplier information may include wholesale price or costinformation for the product (such as recommended prices, tiered pricing,and the like). The suppliers may tend to offer financial support such asto generate higher sales. Such support may come in the way of salessupport, financial incentives (e.g. give a distributor 50 cents per casecash in a bank for each case that are sold) and the like. A solution fordata discovery for operating a business via guided page navigation forsuppliers may focus on financial support return on investment that ismeant to generate higher sales. In an aspect, there may be a notion ofdiscount participation that impacts pricing that a supplier can expectto charge. For example, if a customer buys ten products, then thecustomer may have to pay a price P1 per product, while if the customerbuys 100 such products, then the cost may be discounted and the customermay for example have to pay P2 per product, which is almost assuredlyless than price P1. Similarly, for even higher volume purchases, thecustomer may get an even steeper discount. In an aspect, there may alsobe another notion of receiving some financial support based on the levelof discount. For example if a customer sells at discount level X, thecustomer may receive an incentive, and if the customer sells at discountlevel 2X, then the customer may receive greater incentive. In an aspect,the solution for data discovery for operating a business via guided pagenavigation for the supplier may focus on a discount participationdimension of the data.

The data sources may further include sales person information. Thesolutions for data discovery for operating a business via guided pagenavigation for a sales person may focus or depend on the nature and typeof customers associated with a specific sales person. For example, thesales person of a supplier may sell to distributors or directly to endbuyers (e.g. wine collectors, large retailers, and the like). The salesperson of a distributor may sell to retailers (e.g., restaurants, hotelchains, package stores, grocery stores, and the like). The sales personof a retailer may sell to consumers or entities such as businesses,individuals, and the like. In this aspect, the term “customer” describedherein, may have different meanings for a supplier, a distributor, aretailer, and the like. The distributor or supplier may have its owndatabase of files that may control the salesperson team structure. Thesolutions for data discovery for operating a business via guided pagenavigation for any portion of the wine and liquor sales chain (supplier,distributor, retailer, and the like) may focus on being able to look atany level of hierarchy and dive down all the way to an individualsalesperson.

Sales person data may be characterized by at least two data elementssuch as customer and product type (e.g. wine or liquor). Sales persondata may also be tied to the customer and/or the sales person. Forexample, each customer may be assigned at least two salespersons (suchas for example wine sales person and liquor sales person).Alternatively, a distributor may have a dedicated sales force selling aparticular supplier's products. This may happen when the suppliers arequite large. The result may be that a customer may be associated withboth a dedicated sales team and one or two other sales persons for otherproducts all from the same distributor. For example, there may be aregular wine salesperson and a special supplier wine salesperson suchthat the customer may be associated with regular liquor salesperson,regular wine salesperson, and regular supplier salesperson. Therefore,solutions for data discovery for operating a business via guided pagenavigation may include support for complex salesperson-customerarrangements.

The data sources may further include distributor data, which may besimilar to a supplier data (such as for example, the distributors mayhave their own salesperson that may be selling to the retailers, and thelike). In addition, the distributor may also deal with an inventory thatthe distributor may need to manage (such as product mix, inventory,stock management, lead time for ordering, future sales plans, and thelike). Therefore, solutions for data discovery for operating a businessvia guided page navigation for the distributors may therefore be focusedon inventory management considerations.

As noted above, pricing may be a bit complex for distributors due tocustomer pricing matrix requirements. In an example, a customer pays adefined price P0 for a product when purchased in a particular quantity,and another customer may pay price P2 for the same product at the samequantity. Likewise, a customer's store in state S1 may purchase productat a different price than that same customer's store in state S2 due toa variety of factors, such as state-by-state distribution costdifferences, state imposed minimum purchase fees, and the like). Thesolutions for data discovery for operating a business via guided pagenavigation for the distributors may focus on managing the customerpricing matrix considerations. One such consideration is to comply withstate regulations, a sale by a supplier to an end consumer may need tobe transacted through a distributor (as if the distributor were buyingthe product from the supplier and then selling it to the end consumer)even though the product merely gets received and then forwarded by thedistributor. Alternatively, a distributor may buy the product from thesupplier, and the product may be shipped to a warehouse, held there, andthen sold in a separate transaction or transactions.

In an aspect, the data sources may also include retail customer data andconsumer data. The access to the data sources described herein may be byusing an Open Database Connectivity (ODBC) database and the like. Anon-standard file may be accessed by using an FTP server (such as FTPdata source). The database may include flat files or text, MS Excel®files or MS Access® files and the like.

FIG. 36 shows an example of a domain editor or a data integrator of thesystem for facilitating data discovery for operating a business viaguided page navigation output. The figure describes a sample input datathat may be used as an input to a builder program. The input to thebuilder program may be modeled as a set of flat files. Each flat file50, 52 may consist of a number of records 48, each of which may describea single entity. The records 48 may be divided up into different fields48, which may represent different attributes of the entity. Multiplefiles may be related by using a common field such as to perform astandard relational database join operation to produce a single logicalfile, with one record for each entity.

The system for facilitating data discovery for operating a business viaguided page navigation may be used to describe the fields in the inputfiles, and assign a type to each of the fields. The data dictionary maycontain three pieces of information about each field: a field name 40, afield type 42, and an associated dimension 44. The field name 40 may beused to identify the information contained in the field. The field type42 may identify the field as either a dimension, a summary field, or anon-summary field. The dimension field may be a search key along whichthe data that may be organized and summarized. The summary field may bea numeric quantity that provides useful information when summed andaveraged. The non-summary field may contain information that may beassociated with each input record, or with a value of the dimensionfield. The non-summary field may be a field that may not be importantenough to be a dimension field, or a field that may directly be relatedto an existing dimension field. The associated dimension 44 may be usedfor non-summary fields to identify the dimension field that theinformation is associated with or “Detail” if the non-summaryinformation is unique for each input record.

The example in FIG. 36 illustrates a personnel database, where eachrecord 48 in an employee file 50 represents an employee of a company.The input files may consist of the employee file 50, which may containdata about each employee, and the department file 52, which may containinformation about the different departments. The department file 52 maybe joined to the employee file 50 using a common department Identifier(ID) field. The data dictionary may describe various fields of the inputfiles, and may identify the fields as dimension fields, summary fields,and non-summary fields. The department name and manager fields may benon-summary fields associated with a Dept. ID dimension field. Theaddress and name fields may be non-summary fields associated with eachinput record. Alternatively, the address and name fields may beassociated with the employee ID dimension field, since the employee IDfield is unique for each record.

There may be several challenges associated with database management, andrecord keeping. For example, in an aspect, item number for the sameproduct may be different in different states such as in Florida versusin Missouri. In an aspect, it may be required to summarize productstogether because total sales of a particular brand such as “JohnnyWalker Red” may be required to be determined in a business. Therefore,it may be required to denormalize the data. The single denormalized datarecord for different product numbers, descriptions may be created suchas e.g. the record may hold a “Florida item number” field and a“Missouri item number field.” A cross-reference table may be providedthat may act as a link to pull in the data. The link may exist in thelowest level record, which links to a cross-reference table in the datasource. For example, if the lowest level is invoice line item level fora product P, then product information about the item P such assuppliers, cost, size, taste, packages per case, cases per pack, and thelike, customer information about the sale such as state, county, tax,link to regulations, license, and the like, sales related informationfor a sale such as salesman information, and the like, or any otherrepresentation of the line item such as for the same product that canhave different item numbers in different states may be retrieved.Alternatively, a cross-reference table may be linked such as to allowaggregated reporting across states that may include different data forthe same field. This link may be in the lowest level record. The crossreference may be made to a non-data content item such as an image of adistributor's license tied to the license and the like.

FIG. 37 describes a domain editor such as to discover data and establishdomains of data. The domain editor described herein may be configured tofacilitate data discovery and further facilitate ingestion, integrationof data, and the like. The domain editor may be used to convert datafrom various data sources or types into a format and content that may beused by the model-building engine. In an aspect, an expert may configurethe domain editor to create a single record for each relevant item. Adataset may be created to facilitate the ingestion of data from a rangeof disparate sources to generate models that may facilitate fast, guidedpage generation and dimension-specific data discovery. The datasetdescribed herein may include at the lowest level a single record thatcompletely defines itself for example, an invoice line item level in asales or distribution industry. The line item in the invoice may definethe item, its price, quantity, and the like. The dataset may have enoughinformation in a single record so that all products related information(bottles, pack, price, product, case, and the like), the customerinformation (such as for example name, city, state, zip, on or offpremise, and the like), salesperson information for that sale, and thelike can be pulled. The lowest level of detail may be denormalized datastored in one record. In contrast, a normalized database data may have astar schema with tables for each different type of information (such asfor example one for product, one for customer, and the like) connectedto each other with database joins.

In an aspect, the domain editor may be configured to generate data in aformat and organization that may facilitate a variant of data discoveryfor operating a business via guided page navigation based on modelsgenerated from domain editor data output. It is important to guide datadiscovery on a daily basis such as to know the performance. Theimportant aspects of a business may be product (relationship to theproducts), customer (relationships to the customers), and the like. Thedata discovery phase may enable these aspects such as to provide guidedpage navigation. In an aspect, the information that is not important ata particular time period may be important later for the business. In anaspect, various industry experts may participate and know what to promptthe customer. For example, varying shelf life versus beneficial aging isunique to the wine/beer business. The unique distribution rules (such asexclusive versus non-exclusive that may be allowed to handle somethingor to handle different customer licensing from state to state, such asdetermining customers' preferences towards products, ways and methods ofpurchases by customers, dry counties within a state, restricted licenseswithin a state, county, town, or the like). The rules may take care ofand consider several aspects such as government reporting (such asimports), process for bonded product and pulling it out of bond. Forexample, every state may have different ways of reporting wine andliquor sales. The customer part of the lowest level record may have alicense number in a state or county location and there may be a link toa regulatory requirement in a business.

An industry expert may ask or prompt a client about customer aspectsthat may be important about the customer, product aspects that may beimportant about the product, and the like. A user of the methods andsystems for operation of a business through data discovery via guidedpage navigation may be required to take on license for a product that isdifferent from state to state. This may reflect highly regulated supplychain requirements that may be encoded into and/or impact customerpreferences related to which product to buy in which state and throughwhich channel, region (e.g. dry county, limits, food sales related), andthe like. The rules may take care of and consider several aspects suchas government reporting (such as imports), process for bonded productand pulling it out of bond. Other aspects such as time frame or measures(day, week, month, summarizing data, and detailed data) underconsideration, and ways of comparing data (Year-to-Date (YTD),month-to-month, quarter-to-quarter) may also be considered.

In an aspect, the domain editor may be configured to determine the timebased on the sales periods, days, weeks, months, type of calendar, andthe like. In an aspect, the domain editor may be configured to summarizethe things such as by week, month, quarter, half, year, and the like. Inan aspect, the data discovery phase may determine the vintage (such asprice-sensitivity), product's conditions (such as freshness level ofbeer, as an example), and the like. In an aspect, the system foroperating a business via guided page navigation may consider andfacilitate data discovery related to First In, First Out (FIFO) salesprocess such as to keep things or products fresh. In an aspect, certainissues may be found during the data discovery such as the supplier maydrop the customers from a master customer list if sales targets are notmet. Similar issues may also be found during data discovery forsalesman, product, and the like.

The data discovery tool or phase of data integration supported by thedomain editor and associated functions may be configured to provide acycle where detection that a supplier/distributor may have dropped oneor more customers off the suppliers/distributors master customer list ifthey hadn't been sold to in a particular time (e.g. more than one year),to compare annual records of sales and services (e.g. current and priortime periods), to determine relationships among the customers, and thelike.

The data discovery tool may be configured to provide the information ofthe products that may no longer exist in the database and salespersoninformation that may be no longer associated with current sales activityor a salesperson that may no longer exist in the current businessentity. This may allow an industry expert operating the domain editorrelated tools for suppliers and/or distributors to balance with thenumbers that such a customer thinks may be accurate. The data discoverytool may be configured to use Year-to-Date (YTD) sales and thehistorical sales (such as last year sales), go through the data andmanipulate it (such as add it all up with the relationships and tools)to consider for any association or link or match. In an aspect, thecustomers may often have special logic in totaling reports where theymay skip, substitute, or hard code items, and the like. Such customersmay provide a hard copy of the report from their source system. The datadomain editor module may be configured to use the data from such reportsand databases to compare the data (hard copy of reports sent by thecustomers) against their own internal reports (collected from varioussources) to allow the distributors or the suppliers to keep a balanceamong the products and customer requirements. In an aspect, the thoughtsof the supplier and the distributor may also be compared such as todetermine their needs and maintain as close as substantially 100%balance. In an aspect, the number of products and customer requirementsmay be balanced based on the customer thoughts (such as what thecustomer thinks that are accurate to the actual data they provide).These data may be compared against the distributor database data such toidentify if one more match exists.

FIG. 38 depicts aspects related to a data integration module of a systemfor facilitating data discovery for operating a business via guided pagenavigation. The data integration module may be configured to use thedimensions and other information determined during data discovery. Thedata integration module described herein may be a form of an Extract,Transform, and Load (ETL) Tool. The data integration module may beconfigured to extract data from multiple source types, transforms withjoins, lookups, filters, calculations, and the like, and loads to flatfile format for builder or other uses. The data integration module maybe configured to control scripts written in DI Object Language. The dataintegration module may be configured to include command-line executionand Visual GUI (such as Visual Data Integrator) available.

FIG. 39 depicts aspects related to a visual data integration module of asystem for facilitating data discovery for operating a business viaguided page navigation. The visual data integration module may beconfigured to include a Graphical User Interface (GUI) for dataintegrator and application to create and manage data flow. FIG. 39 showsone embodiment of the visual data integration module during the creationof a data integration control script. Within the task flow panel 3902the user may arrange task objects 3904, which are graphicalrepresentations of processes, inputs, and outputs, and createconnections between said task objects 3904. A side panel 3908 allows theuser to add new task objects 3904 to the task flow panel 3902 forinclusion in the data integration module. The attributes for thedifferent task objects 3904 may be altered. A task object 3904 isselected and highlighted and the attribute values of the task object3904 are displayed in the object editor 3910 shown below the task flowpanel 3902. Within the object editor 3910 the values of the differentattributes may be altered. The visual data integration module mayfurther be configured to facilitate examination of data files, generateor supply script syntax to underlying control scripts, support testingand review of results, and the like.

In an example as depicted in FIG. 39, various aspects related to abuilder or visual builder module of a system may facilitate datadiscovery for operating a business via guided page navigation. Thebuilder or visual builder module may be configured to summarize andpreprocess the source data, transform the data into a model, and thelike. The builder or visual builder module may be configured to includeproprietary data structure optimized for rapid access by “DiverSolution” clients, command-line production builder available, visualbuilder which is a GUI tool, and the like. Further, the visual buildermodule may be configured to be controlled by build description files,run-time parameters, command line execution options, and the like.

FIG. 40 depicts aspects related to models of a system for facilitatingdata discovery for operating a business via guided page navigation. Thesystem may be configured to create industry-specific models that mayhave certain data summarized based on an expert review of the dataconducted during data domain editor operation and use. The data may beprocessed through an automated structuring facility that may be guidedby scripts generated by the visual domain editor. The result may be thecreation of a model optimized to comply with relevant data store needs.In an example, portions of a model or a subset of a set of models may bestored on a server, on a client device such as a tablet device, and thelike. The model(s) described herein may include summary tables that mayrender down to fact tables. The data requests may be sent to themodel(s) as ad hoc or marker (saved reference) with answers beingreturned from the model(s). The model(s) may be stored on or beaccessible in disk, cache, or other types of memory. The models(s)described herein may be memory models, multi-models, and the like. Amodel may consist of a column-based or row-based database or tablemapped into memory for providing fast access of subsets of data andsummarized data.

In an aspect, business rules may be layered onto the models or may beintegrated into the models such as to facilitate access to the data inthe models. This may include information about how data in the modelsshould be used or interpreted. The business rules can set values for thevariables or definitions that may be dealt with in the business. Forexample, “Tiers” can be defined such as to differentiate between a tier1 versus a tier 2 customer. Similarly, “New placements” or “Newproducts” can also be defined such as for a new product to decide if acustomer purchased the product within a defined period of time or later.Business rules may also be associated with or embodied in a diveplanthat may provide further guidance to access and presentation of datafrom the data models. A diveplan is described in further detailelsewhere herein.

Divers of a system may facilitate data discovery for operating abusiness via guided page navigation. In an aspect, a tool called thediver may be used to access the models. The diver described herein maybe a fully free-form data access tool that may be aware of critical datadimensions, and the like found during data discovery. The diver may beconfigured to add multiple data at any location and in anytime. Thediver may be configured to open up a model directly and see coredimensions. Further, the diver may be configured to enablepoint-and-click GUI, support viewing and analysis of model(s), displays,saves, prints data, and the like. The diver may be configured to enablevarious output formats such as tabular, report, graph, live data views,and the like to view the data. The diver may be configured to supportunstructured diving, facilitate creating “Markers” for use in DivePort,desktop (DI-Diver), client-server (ProDiver), and the like.

Application Development

FIG. 41 depicts aspects related to an application development module ofthe system for facilitating data discovery for operating a business viaguided page navigation. The FIG. 41 depicts aspects related to anapplication that delivers information from data collection or a datawarehouse. Delivery of data from a data collection or warehouse may befacilitated by data Integrator, Builder, Modeling (such as models,memory models, multi-models), Data access (such as Diver, DivePlans,Markers), Application-based data access (such as Diveline and Diveport),and the like, which are described herein and elsewhere.

In an example, the diveplans of the system may facilitate data discoveryfor operating a business via guided page navigation. The model and datawarehouse may have a direct “tunnel” connection to enable linkinganything in a model to relevant source data. The dive plan describedherein may be a presentation vehicle diveplan that may be similar tobusiness rules while substantively extending them. The diveplan may beconfigured to present default columns that may be viewed when thecustomer dives. For example, the diveplan may facilitate the customer,distributor, or supplier to view the sales, cases, or gross profit ofthe products. The diveplan described herein may be configured to helpthe customer, distributor, or supplier get access to the data. Thediveplans help to set up a structure (a group called “item” or“customer” so that the customer can open it up and view all of thethings that may be dived on). The diveplan may allow controlling thepresentation of the dynamic dimensions (have “type” show up in thecustomer area even though it is not a core dimension). The diveplan maypick dynamic dimensions that should be presented for each coredimension.

In an aspect, one or more things that may be tied together in a diveplanmay include, for example, Florida sales, Florida (FL) annual revenue(A/R), and the like such that the customer, distributor, or supplier maydive on sales and A/R in Florida.

In an aspect, the diveplan may be configured to control how coredimensions are presented. For example, it may control dynamic dimensionsfor a core dimension. The diveplan may be configured to embed a taxtable or rate based on a state, define definitions for the gross profit,define new placements (such as product sales time frames to define a newplacement), and define discounting tiers (such as product and quantity).

To accommodate different users' needs regarding viewing sales, products,profit, and the like when a diver application is first opened by thecustomer, a diveplan may provide an initial view for each type ofcustomer and/or for each unique user. Even though there may be manyother dimensions or summaries, there may be an initial focal point suchas initial columns to be shown. The desire of viewing the initial focusof point may be different for different functions (such as a salesrepresentative, executive, manager, retailer, and the like may view themdifferently) and a diveplan may be configured for each such function.

As noted above, the wine and spirits industries have differingrequirements across states. To accommodate such differing requirements,separate models may be created for different states such as one forFlorida (FL) and another for California (CA). Alternatively, differentmodels may be created for business functions such as FL sales or FLaccounts receivable (A/R). A diveplan may be used to link individualmodels seamlessly so that they may be presented to a user as if therewas a single corporate level model. In an example, the diver may be usedto open a model directly to facilitate access to the core dimensionsthat may be related to customer or distributor or supplier. The divertechnology may be used along with the business rules and diveplan(s)such as to create useful, direct links among data elements in atablet-based user interface.

Another aspect of the invention is related to canister model of thesystem for facilitating data discovery for operating a business viaguided page navigation. If an end result is to create some special pagesinstead of ad hoc diving, then the system may migrate from such as a 150Gig model to a tablet, or the like such that the user may have adifferent kind of model. The different kinds of models described hereinmay include a “canister”, and others. The domain editor and/orintegration module may be used to pre-calculate time-based columns. Thisis a function that may normally be left to a user using the diverfunction.

Viewing data in a canister model may require a user to select at leastone time frame for viewing data. Essentially a canister model may definetime (or time frames) as core dimension(s). While this may contribute tosome constraining of navigation within the data model, it significantlyincreases response speed while reducing memory storage requirements,both of which are highly desirable for a tablet-based embodiment of themethods and systems described herein. In an example, the system mayguide a user to pick a month (e.g. a time frame), and in response topicking a particular month, the user may be presented a matrix of a listof customers and time frames based on the selected month (e.g. Month todate dollars, YTD dollars, Quarter-to-Date (QTD) dollars, and the like)for the customer. In this aspect, the month is a dimension, customer isa dimension, but the other items are pre-determined. The canister modelmay be configured to combine the customer-month and hard code such as toconfigure the model.

In an aspect, the domain editor may be used to configure an integrationmodule that may be used to create a canister model. The integrationmodule may be configured to define the data to be summarized, the datato be displayed when a user selects a dimension or dimensioncombination, and the like. For example, the month by customer canistermodel may be time-based summaries of certain aspects such asMonth-to-Date (MTD), YTD, QTY, Year over Year (YoY), Quarter on Quarter(QoQ), Quarter to Quarter (QtoQ), and the like. The integration modulemay pre-calculate the time-based data rather than allowing the diver topre-calculate using a time core dimension. In the tablet interface,jumping from screen to screen may be limited by these time-basedsummaries. In order to create a canister model (before the time seriessoftware was developed), the user may had to undergo eight or nine stepsalong with having an extensive knowledge of data integration such as togenerate the time-series summarized data.

In an example, various aspects related to technology components such asDiveLine of a system may facilitate data discovery for operating abusiness via guided page navigation. The DiveLine may include serverapplications. The DiveLine described herein may include a service thatmanages connections, authenticates named users and groups, centralizesaccess to model data, and the like. The service described herein may beflexible and scalable to use.

In an example, various aspects related to technology components such asDivePort of the system may facilitate data discovery for operating abusiness via guided page navigation. The DivePort may include JAVAserver app that may reside on a Web App Server. The DivePort may beaccessed via a Web browser such as to provide an information-deliveryapp (such as portal) based on portlet web technology. The Portalconsists of pages that may contain portlet instances. The system maycreate and configure both the pages and their portlet instances. Thepresentation of data from multiple data sources may be customized.Usually, the user may use full data model when using the diver and theProDiver. In an aspect, when a user uses DivePort, then the user may usethe canister model. The DivePort may be configured to determine that auser selects a time dimension and then accesses the data based on theselection.

FIG. 41 also depicts aspects related to user access to the system forfacilitating data discovery for operating a business via guided pagenavigation.

FIG. 42 depicts aspects related to a configuration module of the systemfor facilitating data discovery for operating a business via guided pagenavigation.

In an example, in conjunction with the modules of FIG. 42, a controllermodule of the system for facilitating data discovery for operating abusiness via guided page navigation may be provided. The controllermodule may include a DiveLine client and window-based user Interfacesuch as to access users and model activity information and control theuser access based on using a “Model”. In an aspect, the controllermodule may disconnect the entire user or disconnect those users who areusing the model.

In an example, in conjunction with the modules of FIG. 42, a schedulermodule of the system for facilitating data discovery for operating abusiness via guided page navigation may be provided. The schedulermodule may include a client app such as to create and automate eventsrelated to the DI products. The scheduler module may be configured tomanage jobs, events, tasks, and the like. The scheduler module may alsobe configured to review current status and history.

In an example, in conjunction with the modules of FIG. 42, a broadcastmodule of the system may be provided for facilitating data discovery foroperating a business via guided page navigation. The broadcast modulemay be configured to include window-based user interface (UI) such as toschedule or event-driven delivery of Diver Markers via email. Further,the broadcast module may be configured to include DiveLine Admin Tooland Power user tool.

In an example, in conjunction with the modules of FIG. 42, an auditingmodule of the system may be provided for facilitating data discovery foroperating a business via guided page navigation. The usage-auditingmodule may be configured to include scripts to select and manipulateDiveLine Logs and build models of the Log data. Further, theusage-auditing module may be configured to examine triggers, DivePaths,columns, user sessions, and the like.

In an example, in conjunction with the modules of FIG. 42, a DIAL moduleof the system may be provided for facilitating data discovery foroperating a business via guided page navigation. The DIAL moduledescribed herein is a high-level programming language that may includeJava-based command line program. The DIAL module may be configured toprocess, analyze, and distribute Model information. The DIAL module mayget connected via the DiveLine. The DIAL module may be configured tofind and flag exceptional data values and send results as email or savedfiles.

Conventional audio/video presentation systems operate on a principlethat information in the presentation will be delivered linearly whichnecessitates that access to the information is linear. The result isthat accessing information that is more than one “slide” apart requirestraversing through slides to get to the desired information. The resultis a visually distracting race through a sequence of slides ofinformation that might have already been presented or has not yet beenpresented. Both of these scenarios may cause confusion for the audienceas well as the presenter. Designers have attempted to alleviate theseproblems through techniques such as providing access to certain slidesthat might start a sequential section of slides or establishing acheckpoint in the linear sequence of slides.

The methods and systems described herein for generating and navigatingthrough guided pages enables unparalleled flexibility for thepreparation of and delivery of audio/visual presentations. These methodsand systems allow a presenter (or simply a viewer) to dynamically choosea path for presenting material, even when the material is configured asindividual screen-sized “slides” that may be similar in appearance to aconventional presentation slide. By enabling dynamic path selection, apresenter may be able to quickly change the presentation view to onethat is pertinent to a viewer's questions and just as quickly return tothe previously selected path.

Unlike linear presentations, presentations based on the methods andsystems of guided page navigation described herein can also take onhierarchical characteristics. Much like navigating between guided pagesuses context and relevance for determining which page to present next,hierarchical characteristics of presentations may facilitate selectionof sub-presentations from higher-level groupings. In this way, any of anumber of slides or groups of slides can be selected by moving up one ormore levels in the presentation hierarchy. In addition, at any givenlevel in a hierarchy, slide selection features in each of the slides mayallow direct access to any of the other slides at that level. Theseslide selection features may be similar to points of entry on the guidedpages described herein that may allow a user to access information fromrelated dimensions directly from each guided page.

The result is a substantially different man-machine interface fordelivering presentations than is available with conventionalpresentation systems.

Other features of the methods and systems of guided page navigation thatmay be beneficial for the dynamic path presentation techniques describedherein may include impact of a workflow on presentation preparation. Inan example, a salesperson may be planning a visit to a customer thatincludes delivering a presentation. A workflow for that salesperson,along with identification of the customer and customer sales history andthe like, may result in relevant information being uploaded to thesalesperson's computer (e.g. tablet device) and organized with guidedlinks that facilitate dynamic presentation path selection. Further inthe example, the salesperson may be presenting information about aproduct that competes with a product that the customer currently sells.The salesperson could switch the presentation view in response to aquestion by the customer regarding the sales details of the currentproduct quickly because the guided page navigation methods and systemswould have configured the salesperson's computer (e.g. tablet) with aset of presentation slides that have guided navigation links to theinformation that is relevant to the salesperson's workflow (selling) andcustomer (sales history by product). However, rather than simplyproviding all of the relevant information and documents to thesalesperson, each document and data element would be coherently linkedusing the techniques described herein for generating guided pages fromdata models based on core dimensions and the like.

The guided page type, dynamic path selection presentation capabilitiesdescribed herein may also be leveraged for specific users, departments,and the like. Corporations often generate information, news, pressreleases, and the like. In addition, corporations often requiredisseminating information to select users while broadcasting otherinformation to a wider audience. The guided page navigation methods andsystems described herein that incorporate user roles, workflow,hierarchy, and the like can be leveraged by the presentation methods andsystems herein to facilitate delivery and broadcasting of news and thelike. In an example of a marketing manager desiring to present news tothe marketing staff and present only a portion of that news to theentire company. A “news” module of the guided page systems describedherein may access the marketing news and provide links among the newsitems that are determined based on the user viewing this news so thatmarketing staff can link to information that others in the companycannot.

Similarly, presentation information can be associated with coredimensions as described herein in such a way as to allow access based onconnections among dimensions. Presentations may therefore be dynamicallyadjusted based on a dimension such as customer. While a general salespresentation may be generated, by a user selecting an entry in adimension (e.g. a specific customer in a “customer core dimension”) thepresentation may dynamically adjust to only include slides that areassociated with the selected dimension and specific entry.

In another example, a sales team may want to generate news for internaluse at a company about a recent big sale to a particular customer. Apresentation about news in the company may show this news item. Uponselection of this news item, context may be directed to the customerdimension and more specifically to the customer in the news item. Theuser could then view guided pages of the customer's sales, shipments,returns, and any other data by region, quarter, location, and the likebecause the guide pages containing this information could be accesseddirectly from the presentation of the news item because the news itemidentified the specific customer dimension value (e.g. customer name).Within the guided pages of the customer specific information, one suchguided page may include entries related to customer specific news overtime (e.g. counts of news stories per quarter). Selection of one ofthese entries may bring up a selection of news items that maybe viewedin presentation mode.

The synergistic application of the methods and systems of dynamicpresentation path selection with the methods and systems of guide pagenavigation each of which is described herein is envisioned as resultingin an integration of business needs including: documents, data inputs,messages, workflows, projects, numbers, and the like with technologyrequirements including: numbers, text, video, pictures, and the like inan interface that results in flexible ways of accessing information foroperating a business around core dimensions of data that is pertinent tothe business operation. The result is envisioned as an interface thatseamless facilitates access to all types of information for operatingthe business.

At least through the application of the methods and systems forgenerating guided pages from disparate source data, which may includeprocessing of the source data by a central server, corporatepresentations may be coordinated so as to ensure that differentpresenters reference corporate information that is consistent across allpresentations. Therefore, the methods and systems described herein mayprovide the significant benefit of ensuring that presentations (or atleast portions, such as specific pages, data, and the like) aresynchronized against data models and centralized data and contentsources. This benefit extends to enabling management to centrally managekey presentation materials and resources so as to help presenters avoidinconsistency of message. However, by the dynamic path selectioncapabilities, each presenter may be enabled to tailor the flow ofhis/her presentation for the audience at hand.

In addition, each presentation, or more particularly, each section of apresentation (which may include a collection of closely related pages,and the like) may be linked through the guided page techniques thatrelate back to core dimensions through the data models, business rulesand other techniques described herein. In this way, presentations maylink to each other to organize the presentations themselves. Suchlinkages may become a meta-presentation that enables each presentationto work along side each other, as opposed to conventional linearpresentations that can be individually modified without considerationfor impact to others. In an example of inter-presentation linkages, aset of presentations for a user group meeting may result in creating atype of “glue” between individual presentations that results in acompany-wide resource of material that can be used to educate customers.

Referring to FIG. 43, various presentation slides that facilitatedynamic path selection are depicted. In particular, slide 48 shows someof the features described herein. A left arrow allows for directlylinking to the previously presented slide even if the previouslypresented slide is not linearly the previous slide. The previous slidemight have been a slide about technology generally or the like. Otherselectable elements along the bottom of the figure implement the dynamicpath selection described herein. Each of “opportunity” “di mobile”,“knowledge”, and “workflow” may link to a slide somewhere else in thepresentation. In this example “opportunity” links to slide 49, “dimobile” links to slide 48, “knowledge” links to slide 51 and “workflow”links to slide 52. In this way, a presenter can select any of thesetopics to discuss with the audience from the single technology slide 50.

FIG. 50 may also depict how core technologies grouped as “The DiverSolution 6.4” that harvest data from data sources can interact with“IPAD Design” technologies that interact with “IPAD Process” and“Document Management” to deliver updated guided pages on a regularbasis.

FIG. 43 depicts an exemplary top level showing a selection of dynamicpaths to follow.

FIG. 44 depicts an exemplary guided page view of distributors that maybe suitable for use in a presentation that may be presented when the“Distributors” tab is selected in FIG. 43.

FIG. 45 depicts an exemplary slide of product logos that may bepresented when a “Product” tab is selected in FIG. 43.

FIG. 47 depicts an exemplary slide of Web PDF data that contains linksto other non-linearly presented data.

FIG. 48 depicts an exemplary presentation menu slide that may bepresented by selecting the “presentation” tab in FIG. 43.

Methods and systems described herein may include dynamically navigatinga presentation that comprises a set of guided pages of content whereingroups of presentation pages are linked to other groups of presentationpages through association with a set of core dimensions that aredetermined during a data discovery process and that are common to thepresentation pages. The presentation may include content pages, datapages, and hybrid pages wherein actionable elements in the presentationpages link to other presentation pages based on the core dimensions.

Methods and systems described herein may include automatically linking aplurality of presentations that are organized around a set of commoncore data dimensions that are extracted from disparate source datarelated to operation of a business.

Information Leaping

The methods, systems, technology, and techniques of guided pagenavigation described herein may be applied to presentation type pagenavigation to facilitate information leaping. Information leaping maygenerally be characterized as quickly moving from one information domainto another information domain. In the context of page type navigation,information leaping may be characterized as rapidly moving from a firstspot in a presentation of information to another related spot that maynot be physically close to the first spot (e.g. may not be the previousor next slide in a presentation). The guided page navigation disclosureherein may facilitate such information leaping in businessintelligence-related presentations through various techniques related todetermining a set of possible destinations associated with informationon a given presentation slide, selecting a subset of the possibledestinations, and enabling links to the selected subset of possibledestination (e.g. tabs, selectable content, and the like) on the givenpresentation slide that rapidly shift the information focus (e.g. bypresenting related information) when selected. In essence, informationleaping may require techniques that facilitate determining in advancewhere each link of active content and menu item (e.g. tab) will leap toso as to arrive at a right place.

Unlike a conventional presentation format that enables linearforward/back information viewing with limited direct access (e.g. basedon a table of contents), a presentation environment configured withinformation leaping facilitates accessing any content within apresentation in as little as 2 to 3 taps/clicks. Two navigation relatedaspects of the present embodiments of information leaping thatfacilitates this great accessibility to information, are the use ofactive content within a presentation format, and the use ofpreconfigured tabs that facilitate rapidly changing a view ofinformation presented in a given presentation slide. Another aspectincludes specific presentation page types, such as access, central, andlist page types. A third aspect includes cross technology conversionthat facilitates converting a conventional linear progressing slidepresentation (e.g. .PPT format) into a bookmarked presentationenvironment that is further adapted for viewing on an electronic displaydevice, such as a tablet or smartphone.

Information leaping may facilitate success in a situation in which apresenter is posed a question and within a few clicks (e.g. two orthree) leap from a current presentation slide or information view toanother information view that is directly related to the posed question.Because the presenter quickly navigates to a desired information areabased on a posed question, the viewer may recognize or conclude that themovement to the new information is not purely through a canned processthat shows generic information.

Like conventional linear progression slide presentation, informationleaping capable presentations that may be enabled by the guided pagenavigation methods and systems described herein may facilitate rapidaccess to various other types of media (e.g. images, video, livestreaming, documents, and the like) as embedded items, or as externallyaccessible items that may employ media-specific viewers, and the like.Information leaping to/from various media types may be accomplishedthrough accessing active data items, menu items, and the like.

Generating a Presentation that Facilitates Information Leaping

Generating a presentation that supports information leaping may includevarious process steps, such as data gathering, organization, and thelike. A portion of the steps that may be included relate to crosstechnology conversion of presentation information. In an example ofgenerating an information leaping capable presentation, process stepsmay include creating a conventional set of presentation slides (e.g.using Microsoft Powerpoint™), saving the slides to a portable documentformat (e.g. .PDF) document, applying guided page navigation techniquesto convert/generate bookmarks (e.g. Acrobat compatible bookmarks),generating a tab for each bookmark and inserting an information relevantsubset of tabs to be presented with (e.g. at the bottom of) eachpresentation page, and providing access to the finalized presentation.Access may be by downloading the presentation to a device, accessing thepresentation from the cloud with a device, and forwarding thepresentation via email attachment. The step of generating tabs frombookmarks may include using a software program that is adapted toextract bookmark metadata from a .PDF automatically generated table ofcontents. A user of such a software program may manually organize thetabs and associate the tabs with content on each presentation page.

Indicators

A cell may contain an alphanumeric item and/or an indicator. Indicatorsmay take on a range of colors, patterns, shapes, logos, icons, labels,characteristics, and the like. Examples of indicators are depicted inFIGS. 53 through 61 and are described here. An indicator may be agraphical element that is used to represent an alphanumeric value, anumeric value or range graphically. An indicator may also consist of adifferentiator for a cell entry, such as a particular color or the like.FIG. 53 depicts using color as indicator that is based on a comparisonof a cell content to a reference, such as whether the value is above thereference. Color indicators in a first cell may be determined by cellcontent in a related cell. An indicator may consist of an alert, whichmay be a colored icon where the color is based on a set of thresholds ona numeric value as shown in FIG. 54. An indicator may consist of abullet graph as depicted in FIG. 55, where numeric values are used todefine the bullet length, a threshold displayed as a line, and athreshold displayed as a bounding box. An indicator may consist of animage or set of images that is present or chosen based on a numericvalue as depicted in FIG. 56. An indicator may consist of a slider,which is a graph (typically aligned on a line) with an alphanumericnumeric value displayed as text and positioned within a range(represented by the ends of the line) based on its value relative to therange as depicted in FIG. 57. An indicator may consist of a plus-minusgraph, where a positive or negative value is displayed as a graph andcolored based on whether it is positive or negative as shown in FIG. 58.An indicator may consist of a racetrack, where a set of stop/go “lights”or colored icons is chosen based on the value in the cell as depicted inFIG. 59. An indicator may consist of a rectangle, where a rectangle isdisplayed with colors based on a set of thresholds for the numeric valueas shown in FIG. 60. An indicator may consist of an arrow whose colorand direction is set as a function of the value of a cell as shown inFIG. 61. Other colors, shapes, and meanings of indicators arecontemplated and included herein.

Column Expansion

An embodiment may include an interface for displaying additional columnsor rows using an expand capability. These additional columns or rows mayrelate to the expanded column or row, allowing deeper analysis byproviding context to the numbers in that column to improveunderstanding.

FIG. 62 displays a tablet matrix of cells, including columns withattribute data and expandable columns whose header includes a chevronicon (>>) indicating that the representation of the data in the columnmay be expanded or altered in some way. Tapping on an expandable columnheader brings up a menu (FIG. 63) comprising various options including“Sort Up”, “Sort Down”, “Graph Column” and “Expand.”

Selecting “Expand” from the menu (FIG. 63) results in an additional setof columns being displayed (FIG. 64) which provide additional datarelated to the selected column. The example (FIGS. 62-64) shows a columnlabeled “Populations in Millions” being selected and expanded. Theresultant display (FIG. 64) in the example includes additional columnsof statistics such as “Median Income”, “Health Spending Per Capita”, “%in Poverty”, and “% on Food Stamps” and “Unemployment Rate.” Theadditional columns provide extra information that does not need to bepresent in the initial display.

The last of the expanded columns, “Unemployment Rate” in the example(FIG. 64), includes a reverse chevron icon (<<) indicating that therepresentation of data in the column may be shrunk or altered in someway. Selecting the header with the reverse chevron brings up a menu(FIG. 65) comprising various options including “Sort Up”, “Sort Down”,“Graph Column” and “Shrink.” Selecting “Shrink” from the menu (FIG. 65)results in the additional columns disappearing, reverting back to theoriginal display (FIG. 62).

Selecting “Graph Column” from the menu (FIG. 66) results in theappearance of an additional column next to the selected numeric column.In the additional column a graphical representation of value appears(FIG. 67) providing a visual representation of the numeric data. Thisoption can be enabled by default for any numeric column. This allows agraphical representation to be available for numeric columns in theinterface without additional setup.

In another embodiment, tapping on an expandable numeric column headerbrings up a menu showing additional options including “Rank Column” and“Percent Column.” Similar to “Graph Column,” these selections result inthe introduction of additional columns providing another way of lookingat the same numeric data (FIG. 68). “Rank Column” results in theaddition of a column showing the numerical rank of the row's valuerelative to the other values in the column. “Percent Column” results inthe addition of a column showing the % of the total column valuecontributed by that row's value.

Numeric expandable columns may be expanded along a variety of dimensionssuch as time as in “Expand Prior Year” and related numeric metricsresulting in the display of additional, rather than just transformed,related numeric data. In the example (FIG. 69) “Net Sales YTD” may beexpanded to show a variety of selectable related numeric metrics such as“Expand Cost”, “Expand Depletion Allowance”, Expand Gross Profit” and“Expand Discount.” Selection of “Expand Gross Profit” results in thedisplay of new columns (FIG. 70) including “Gross Profit YTD”, GrossProfit LYTD″, and the year on year changes such as “Gross Profit YTDDiff” and “Gross Profit YTD % Diff.”

Expansion of numeric columns in the time dimension may include showingthe same data over different time spans or different time intervals. Inone example, a column of sales for a year can be expanded into multiplecolumns of sales, one for each month of the year. Alternately, a columnsuch as “Net Sales YTD” may have an option to “Expand Prior Year” (FIG.69) whose selection would result in the addition of a column of prioryear YTD sales at the same point in the earlier years.

The interface may also be enhanced so that columns that have been addedthrough expansion can be expanded themselves into additional columns.This forms a tree of expanded columns based on the original column, witheach expandable column forming a node of this tree. This allows the userto navigation through data columns through column expansion.

The schema by which numeric columns may be expanded to show additionalcolumns of information is straightforward. In one embodiment (FIG. 71)the title of the initial expandable column is followed by a plurality ofmultiple row groups. Each group of rows comprises a menu title such as“Expand Prior Year” and the columns of data to be displayed uponselection.

Photograph Entry and Use

An embodiment may include the ability to take, upload photos andassociate them with data being displayed. For example, when displaying alist of products, there may be the ability to display photographs ofthese products, their location in a store, and any advertising displaysrelated to them. The mobile device may allow the end-user to take thesephotographs, and upload them the next time the device is connected tothe back-end server.

FIGS. 72 through 77 demonstrate the use of an interface for taking anduploading photographs. FIG. 72 displays a tablet matrix of cells, withelements being able to respond to a photo request as click-actions. Inthis embodiment, icons with a picture of a camera can be tapped to bringup a standard photo taking display as depicted in FIG. 73. The systemmay be configured to have other display elements, such as a missingimage, capable of being tapped to take a photo. After the user takes aphoto, the photo is stored locally so that it is ready for upload to aback-end server at a later time. By storing the photo locally, manyphotos can be taken in rapid succession, such as for different products,without having to wait for individual uploads to complete in betweenphoto shots.

After the user has taken a number of photographs, they can switch to theReview Pending Uploads screen by selecting the menu item depicted inFIG. 74 to view and edit photographs, which are pending. Upon selectionof the Review Pending Uploads menu items depicted in FIG. 74, a screenlisting photographs that are waiting to be uploaded may be displayed asis depicted in FIG. 75. By clicking on individual photo list entries theuser can access an Edit Pending Upload Settings interface as depicted inFIG. 76 to change photograph parameters, such as the file name, editmeta data that may be displayed in the listing of photographs, and thelike. The Edit screen may also include save options, such as to allowoverwriting an existing file.

Once the user has editing any pending uploads, the user can tap the syncbutton and upload the pending photos to the server, where thephotographs can be attached to data, for display in a tablet matrix,such as that depicted in FIG. 75.

The photographs that are taken may be tagged with metadata identifyingthe original data element touched to take the picture, so that theserver will be able to associate the photograph with that data element.This metadata may be displayed in the photograph list and pending uploaddisplays.

Photographs, while in some ways may simply be another form of content,may provide context and scope (e.g. depth) of information that mayaugment alphanumerical data. Photographs are useful for collectingdetails that exist at a point in time during a transient process, suchas production, retail sales, service delivery, and the like. For theseand many other reasons they may be beneficial to operation of abusiness. However, without proper linkage and ways of associatingphotographs to each other and to the important business data thatfacilitates operating a business, their benefit can be missed.Therefore, by using the methods and systems described herein for linkingdata and other content items (e.g. photographs, video, publications,reports, and the like) to develop guided navigation pages for operatinga business, value and benefits of photographs to a business operationmay be substantively increased.

Exemplary scenarios in which photographs may be beneficial, whenproperly combined with operational business data, may include retailstore fronts, shelf allocation, product placement, insurance claimhandling, quality control and improvement, equipment maintenance,production line disturbance analysis, returned/damaged materialsassessment, injuries, and many others. By taking a photograph andlinking it with a range of data dimensions, including central dimensionsas described herein, the methods and systems for operating a businessbased on guided navigation among information screens, deeperunderstanding of transient events may benefit. Photographs may alsoprovide evidence of service delivery (e.g. signage installation),performance over time (periodic photographs of returned materials), andthe like.

By combining photographs with linkable elements, such as centraldimensions and various data domains, photographs that are collected andtagged as linkable content items may address handling of exceptions,such as during production. In an example, photographs of a break down ofa production machine on a factor floor may be linked to a machinedimension, a product dimension, a customer order dimension, a preventivemaintenance dimension, a service claims dimension, an enterpriseresource planning dimension (e.g. production planning), and the like.Therefore when an equipment service provider is using the linked guidedpage methods and systems described herein he may select data itemsrelated to a customer's service requirements (e.g. factor visits torepair equipment) and gain access to the photographs associated with acustomer, a machine, a service contract, production cycle data, anin-service date, a production machine lot code, and the like. In thisway, if the same equipment is serviced at several different customerssites, commonalities among the customers (e.g. machine type, timein-service, etc.) may be leveraged to determine equipment failuresthrough the use of photographs taken when an equipment failure occurs.

Another exemplary use environment for photographs may include productreturns. A photograph of a returned product may be linked to a widerange of business operation items, such as a product part numberdimension that can tie into a company's ERP system, a production lotcode dimension, a production line/machine dimension, a production batchdimension, a raw material dimension, a customer and/or type of customerdimension, and the like. In addition a library of photographs may becollected and analyzed through association with various dimensions thatmay be linked to the photographs in the library. When analysis of areturn is performed, photographs from the analysis may be added to thelibrary and linked to these and other central dimensions.

While photographs may not have any inherently linkable information,merely linking a photograph to a data dimension (e.g. production lot)may, through the existence of associations between a production lotdimension and many other dimension and domains, many of which aredescribed herein above, a photograph may take on a large number ofvaluable attributes and may be accessible from a large number ofdimensions. As noted above a returned item serial number associated witha photograph, may facilitate linking the photograph to a batch of rawmaterials received from a supplier, thereby potentially increasingeffectiveness of communications with the material supplier.

Other use environments for the methods and systems related to photographuse described herein may include inventory management, productplacement, OSHA related compliance and reporting, human resourcecompliance data for accidents, existence or maintenance records ofsafety signage, any kind of service claim, and the like. A time seriesof photographs may be quite useful in substantiating sales revenuenumbers, and the like.

The methods and system for configuring guided pages that are describedin this document include methods and systems that may be used forconfiguring a set of guided pages for operation of a business activitybased on a workflow for the activity, industry expertise, and amulti-domain, multi-dimensional, columnar-based data set derived fromsource data that may be relevant to the business activity. Thecolumnar-based data set may be derived through a data processing engine,referred hereinto as “Spectre” that may facilitate building the data setfrom the data sources using a highly efficient computing methodology.

Data and formatting information for each guided page in the set ofguided pages may be preconfigured and stored in one of a plurality ofguided page caches that may reside on a server and on a client device,such as a tablet-based computing device. In this way, a referring guidedpage of the set of pages my present in the tablet-based user interface,active elements that may facilitate (via user clicking in thetablet-based user interface) access to a next or following guided pagethat may be selected from the set of guided pages. Likewise, based onuser interaction with an active element of a presented guided page onthe tablet-based computing device, data needed to produce a next guidedpage may be dynamically generated through a server-based operation ofSpectre described herein as a “Dive”. Unlike the preconfigured guidedpages, a Dive may operate Spectre through a data query-like function touse a highly computation resource efficient process to analyze thecolumnar-based data set to produce the data needed for the next guidedpage. In this way, a set of guided pages may not require that all dataneeded for such guided pages be preconfigured and made available to aguided page generation capability on the tablet-based computing device.

In addition, each page of the set of guided pages may be organizedaround one or more industry-specific data dimensions. In addition, thecolumnar-based data set may be structured for rapid access by theSpectre data processing facility to generate a portion of the set ofguided pages. In addition, at least one of the one or moreindustry-specific data dimensions may be a core dimension. At least oneof the one or more industry-specific data dimensions may be a dynamicdimension.

The methods and systems for guided page generation described herein mayinclude preparing a columnar-based multi-domain business intelligencedata set, from a plurality of sources of data that can be independentlyformatted, by processing the data from the plurality of sources with adata calculation engine that organizes the columns to align with userspecified and/or automatically determined dimensions that are associatedwith a workflow of a business and populating the columns with data thatrelates to each of the dimensions from each of the plurality of sourcesof data. The columnar data set may be prepared through a process ofsteps that may include parsing source data, harmonizing source dataformats, normalizing strings of the source data per language of a localeor industry specific jargon, performing binary encoding and packing ofnumber values, preparing labels that may act as links for attributes todata entries, sorting strings, and the like.

The Spectre engine may reference a semantic knowledge plan, referredhereinto as a “c-plan” that may indicate various data analysis functionsand summary computations to be performed when accessing the columnardata to produce data for one or more guided pages in a set of guidedpages. A c-plan may comprise a library of common data query expressionsto add semantic knowledge to the columnar data base. A c-plan may beutilized in a guided page set production mode to facilitate the Spectreengine predicting requests for each client device based on prior “dive”requests from any of a plurality of client (e.g., tablet-based) devices.Prior dive request may be from the specific tablet-based device, anotheruser, another party in a similar business or situation (e.g., requestsfor a first wine and spirits client may be predicted based on priorrequests made by a second wine and spirits client). Prediction of diverequests may be based on changes to source data sets. Such predictionsmay derive from machine learning of the types of source data changesthat tend to prompt more dive requests. Such prediction of requests mayresult in generation of optimized, machine-level code for highlyefficient computation of columnar data set query data results. Thec-plan may further be useful for facilitating ad-hoc dive request queryexecution and synchronizing of server and tablet data stores and resultcaches. As an example of ad-hoc dive request query execution, a usermay, such as through selecting an active element in a guided page,generate a request to a guided page formatting tablet function thatrequires accessing data that is specific to the request. In sequence,the client query result cache is first checked for data that may satisfythe specific request. Next the request may be transmitted to the serverwhere the server query result cache may be checked for correspondingdata. Lastly a server calculation using the Spectre engine that mayreference the c-plan to access relevant data in the columnar data setmay be performed and cached on the server. This server-generated resultmay be transmitted to the client in response to the query and stored inthe client (e.g., tablet) result cache.

Spectre performs columnar data set generation (e.g. building orproduction operations) and analysis of the columnar data it hasgenerated using highly efficient processing mechanisms, including queryoptimization and machine code optimization and execution. Generating aplurality of guided navigation pages may be performed by the methods andsystems related to Spectre described herein in response to a userinquiry to provide guided navigation pages to satisfy data viewingrequirements of a workflow of a business. This may be performed byprocessing data with a processor that accelerates page generationoperation through utilization of native machine memory caching ofsimilar c-based data. In addition to caching the columnar-data, the userinquiry includes logical data access instructions that are convertedinto machine-specific code to further improve computer performance. Themachine execution of a user query may be optimized to group queryfunctions into common execution threads based on usage of common datasets by different functions, and to group common calculations acrossquery functions.

Machine memory caching may be optimized with columnar database sincesimilar data is physically proximal in a columnar data set; thereforeaccessing columnar data results in similar data also being accessed andstored in machine memory cache. The result is improved computerperformance. Machine code compiled query and calculation expressionsfurther improves performance of a server or other computer executing theSpectre methods and systems described herein at least by more directlyusing the computer's native processing capabilities. Dive requests(e.g., queries) may be analyzed to optimize machine execution further,such as by determining subsets of calculations that are common for queryfunctions; and grouping query functions into common execution threadsbased on usage of a common data set by more than one query. It is notethat dive queries may have many more expressions than typical SQLdatabase queries; therefore query function analysis and optimization mayhave unexpected measureable benefits compared to prior art data basequery processing techniques. This is evidenced at least in that machinecode query compilation is atypical in relational database usedeployments.

Additionally, generation of a dedicated database for generating guidedpages, e.g., a Spectre generated columnar data set or database furtherenhances dive request execution since the dedicated database isstructured/optimized/tailored for diver operation. This is in strongcontrast to most existing business data intelligence solutions that relyon third-party generated databases. Such reliance reduces complexity forthese solutions, but increases processor load and execution time.

Spectre further enhances the performance of a computing system on whichit is executing through reduction of per-user machine resource overhead.In an example, per-user machine resource overhead reduction may comprisestateless query execution that may access query-specific data in anative machine cache memory, wherein the data is stored in the machinecache as a result of execution of accessing data in a columnar databasethat is proximal to the query-specific data. In an example of astateless query engine, all query-specific state information can bedisposed of after the query is run (e.g., does not persist in memory).In a further detailed example, only query results and any machine nativememory management/caching results stay in memory; such as a result tableand the columnar-based data set itself that is cached by the nativememory management capabilities of the server.

An exemplary deployment environment of Spectre may be in combinationwith a visual data integration configuration user interface that isadapted to facilitate automatically suggesting guided page data queryfunctions, such as by visually presenting data processing functions asgraphical elements rather than merely drop down lists of commonly usedquery functions that are prevalent in text-based interactive developmenteditors (IDEs).

Functions for producing data for use in sets of guided pages that may beperformed or enhanced by Spectre include production build of acolumnar-based data set; grouping; time series, multi-tab, dimensioncount, multi-model, and the like. In an example a time series enginethat targets summary descriptions (e.g., revenues computed over manytime ranges) to generate sets of queries that build on common data usagemay benefit from query analysis and machine code optimizationcapabilities of Spectre that are described elsewhere herein.

Unlike prior embodiments for generating guided pages that relyextensively on human definition of dimensions due to a practical limiton the number of dimensions that can be handled effectively with mostdeployments, Spectre facilitates dynamic dimension definition due to itsefficient operation that is independent of the number of dimensions in adata set. Spectre effectively scales its performance with the data setsize and number of dimensions. This lends Spectre to be an effectivedata evaluation tool for businesses trying to determine what dimensionsof the various data sources that are required to perform variousworkflows may be most useful. Since there is no limit on the number ofdimensions that can be processed, Spectre itself is an excellent tool tofacilitate discovering what dimensions are important to aclient/business function, and the like. This flexibility allows users,such as individual group managers or the like to configure local orprocess flow-specific data sets, even if a dimension that is importantto the user has not previously been defined when the data set wasoriginally set up. A method of business process relevant dimensiondiscovery can include generating a guided page client data set with avery large number of dimensions, evaluating how guided pages using thosedimensions are employed by users, and configuring a production “dive”c-plan based on that evaluation.

The capabilities, functions, and features of the methods and systemsrelated to use of a Spectre-like data processing engine are furtherdescribed in the following exemplary embodiments.

Referring to FIG. 78, an architecture that incorporates the Spectre dataanalytics and processing engine described herein is depicted. Datasources are processed through a data integrator to address differencesin formatting, and the like. This integrated data is further processedby the Spectre engine to build a columnar database that is suitable forthe highly efficient data processing and guided page generationfunctionality described herein. This columnar data set, depicted as“C-Base” can be processed by scripts to deliver data through anappropriate interface to a client-based embodiment of a guided pageapplication, here depicted as “DiveTab”.

Referring to FIG. 79, a data flow diagram for a Spectre-based deploymentis depicted. Data sources, such as databases and text sources arereceived by an embodiment of Spectre that builds a columnar data set asdescribed herein. The Spectre engine further processes the columnar dataset along with c-plan files and other guided page configuration relatedfiles (e.g., lookup files). The output of this further processing maycomprise query cache on a server on which Spectre is hosted (e.g.,Diveline server cache). A dive request server operating on the servermay communicate with the query cache and a client device interfaceserver (e.g., Divetab Server) to satisfy queries and synchronizationoperations with client devices. Data brought over from the server may bestored in a client query result cache that may be the first resourceaccessed when an active element in a referring guided page is accessedfor producing a next/follow-on guided page. At least three types of dataoperations are depicted. Build operations are performed by the Spectreengine to generate the columnar data set. The Spectre engine performscache seed operations to generate query result data for storage in aserver cache. Sync operations are performed between a client device(e.g., a tablet-based device with a guided page application) and theserver.

Referring to FIG. 80, an exemplary data flow diagram representative of aserver query cache lookup is depicted. In step 1, a client guided pageapplication sends a dive request to a server. In step 2, the request isanalyzed and a cache freshness token is evaluated. If the tokenindicates that the server cache data may not be acceptable to meet thedive request of step 1, the Spectre engine is activated to perform thequery. In step 3 the query cache token time stamps are evaluated. Herethe result indicates that the columnar data set (CBASE) and c-plan(CPLAN) token elements are acceptable, but the lookup token is not. Instep 4, the Spectre engine produces updated query results that arestored in the server cache during step 5. The client application, andassociated guided page is updated with the Spectre generated queryresults in step 6.

Referring to FIG. 81, an exemplary columnar database embodiment isdepicted. A columnar-based data set may include a header, columninformation, string tables for mapping indexes into result stringvalues, calendars, data, and properties. In the example of FIG. 4,column 1 may include a name and type. Data in column 1 may includecertain strings, custom calendars, binary data, properties, and thelike. Similarly scoped data may be found in columns, 2 through N.

Referring to FIG. 82, an alternate view of an embodiment of aSpectre-based deployment is depicted. In this embodiment, the elementsdepicted and described in FIGS. 1 and 2 herein are represented, alongwith a native server cache, production dive storage, and multiple clientdevices. Also depicted is a logical ad-hoc dive interface between thequery analysis capabilities associated with the Spectre engine and alogical synchronization interface.

Referring to FIG. 83, an example query analysis and machine codegeneration flow chart is depicted. In the example of FIG. 83, an“AVERAGE” query function is analyzed. First the query function issimplified. The simplified function is processed through a codegeneration facility. The generated code is compiled for the particulartarget server processor to produce highly efficient machine code. Lastlythe target processor executes the machine code.

Referring to FIG. 84, alternate embodiments of the query analysis andoptimization capabilities described herein are depicted. In theembodiment of FIG. 84, a user query is optimized through parsing,followed by expression analysis, data usage analysis, and machine codegeneration. Machine code generation may be targeted to a particularmachine through machine metadata that may describe the particularmachine (e.g., a compiler reference). The result may include a pluralityof machine code executable threads that may be organized to group commonexpressing in a single machine executable thread and to groupexpressions that access common data into a single machine executablethread.

Below is an exemplary columnar data set build algorithm that builds acolumnar database called “demo_drl.cbase” with seventeen columns fromtext input file “demo_drl.txt”.

  build {  text-input “demo_drl.txt” {   column “Order Num”type=“string”   column “Date” type=“date”   column “YearMo”type=“period”calendar=   “gregorian month” format=“YYYY/MM”   column“Quarter” type=“period” calendar=   “gregorian quarter” format=“YYYY/Q”  column “Customer” type=“string”   column “Customer Name” type=“string”  column “City/State/Zip” type=“string”   column “SIC Description”type=“string”   column “Salesperson” type=“string”   column “SalesRegion” type=“string”   column “Product Family” type=“string”   column“Product Name” type=“string”   column “Units” type=“integer”   column“Cost” type=“fixed100”   column “Revenue” type=“fixed100”format=“$#,#.00”   column “SIC Code” type=“string” required-dimension=  “SIC Description”   column “Address1” type=“string”required-dimension=   “Customer”  }  output “demo_drl.cbase” }

Below is an algorithm of an exemplary c-plan using the columnar database“demo_drl.cbase”. This algorithm produces time series summaries of fiveitems over a year-to-date time range.

cplan {  input “demo_drl.cbase”   calc “Order Count” ‘dimcount(“OrderNum”)’   calc “Customer Count” ‘dimcount(“Customer”)’   time-series {  date “YearMo”   // Anchor at the end of the last month we can find inthe cBase   anchor ‘period_end(max(value(“YearMo”)))'   summary “Units”  summary “Cost”   summary “Revenue”   summary “Order Count”   summary“Customer Count”   ranges {   year-to-date   }  } }

Below is an algorithm of an exemplary dive (client data query) of thecolumnar database generated by the algorithm above based on the c-planalgorithm above. The dive is to request three columns of data within a“Sales Region” dimension.

  dive {  cplan “demo_drl.cplan”  window {   dimension “Sales Region”  column “Order Count”   column “Units YTD”   column “Revenue”  } }

An exemplary embodiment of a fast time series c-plan is depicted in FIG.85. A time context compare generator may generate a time series for apoint in time to produce a plurality of values, a comparison (e.g., adifference or delta), and a measure of the comparison (e.g., apercentage of the difference). In the fast time series c-plan example, awine and spirits distribution data set may be queried to count cases anddollars from a plurality of data sources including actual results,forecast results, an initial plan, and an updated plan. The c-plan mayperform comparisons of a current and prior time periods selected from aset of time periods including days, months, calendar quarters, year todate, rolling 3 time units (days, months, and the like), and/or rolling6 time units.

Guided Page Type Relationships

Guided pages may be configured in an informal hierarchical structure asdepicted in FIG. 86. An active element in a guided page, such as a dataelement may preferably direct a user to a first page in a section, suchas an overview page of a section. Pages within sections may be directlyaccessed. Such pages may include an overview page that may link to listpages, a plurality of access pages that link to central pages, and aplurality of list pages that may link to other list pages or centralpages. Pages that are accessed within a section may include a centralpage that shows all relevant information about a dimension, list pagesthat list dimension values off of a dim-count. Typically only onecentral and one list page is prepared per primary dimension. Overviewpages and access pages may also be accessible within the pages of asection.

Guided Page TOC and QV Location

Exemplary guided pages that are configured with differing table ofcontent positioning based on an association of the query values used togenerate a current page and query values used to generate companionpages in a set of pages. In an example FIGS. 10 and 11 depict differenttable of content positioning. In FIG. 87, a table of content is placedat a top of a guided page when query values differ from page to page.

In the example of FIG. 87, a first guided page query value is based onshipments and a second guided page query value is based on market share.Therefore, the table of content is presented at the top of the guidedpage above the query value. This connotes to the user that while the twoguided pages share a common top line content reference, the queryvalues, and therefore the content of each page is different.

In the example of FIG. 88, a set of guided pages share query values sothe query values are positioned above the table of content. Thisconnotes to the viewer that the set of guided pages share common queryvalues but present different content within those query values.

Multi-Column

The methods and systems of columnar-based data set generation and usevia a highly efficient data analytics engine that leverages nativemachine capabilities, such as native machine caching of proximal data inthe columnar data set and machine code generation and execution ofoptimized data query functions may facilitate dynamic expansion of datapresented in a single guided page that represents dimension counts for aplurality of instances of a given dimension. Unlike prior art businessintelligence analysis and viewing implementations that could not producedetails of summary totals of dimension counts (herein “dim-count”)without having to regress through the data generation process tooriginal source data or pre compute pages with all possible breakouts ofdim-counts, the Spectre-based data processing capabilities describedherein provides a dynamic, near real-time dim-count breakout capability.

In an embodiment, the methods and systems described herein for guidedpage generation and use may comprise one-click dim-count expansion anddisplay of a guided page by a user clicking a multi-column activeelement in a referring guided page to produce a dim-count expanded viewof the referring guided page data in a next guided page.

A multi-column capability may be embodied in a matrix that presents aplurality of data domains in a first column and a count of alloccurrences of a dimension for each data domain in a second column; eachdata domain row is split into multiple rows that represent uniquecombinations of each data domain and a unique instance of the dimensionso that the second column shows only a total of occurrences of eachinstance of the dimension for the specific combination of data domainand dimension instance.

Alternatively, a multi-column capability may be embodied in a matrixthat presents a plurality of instances of a first dimension and a countof all occurrences of a second dimension for a first domain instance ina second column, each first dimension instance row is split intomultiple rows that represent unique combinations of each first dimensionand a unique instance of the second dimension so that the second columnshows only a total of occurrences of each instance of the seconddimension for the specific combination of first dimension and seconddimension.

In an alternate embodiment, a dim-count matrix may be presented in aguided page that may include column headings that comprise dimensions ofthe data and rows that comprise domains of the data. A multi-columnfunction may take any dimension that is displayed as a column headingand create rows that represent unique combinations of each row domainwith the selected dimension so that the number of rows formed peroriginal domain row equals the dimension count (dim-count) in each cellin the multi-domain activated dimension column. Further in thisembodiment the dim-counts for all of the other columns in each originalrow are divided properly to the resulting individual combination rows sothat the total of dim-counts in the resulting combination rows equalsthe dim-counts in the original non-combined row.

A multi-column capability logically involves separating a dim-count,which is a summarized count of data elements that are identified by adimension (and likely further identified by some other data aspect, suchas another dimension or a data domain) into counts for its individualdata elements. In an example a dim-count represents premium sales persalesperson. The data elements that are used to compute the dim-countare customer sales, so the dim-count is a total of premium sales acrossall customers to whom a premium sale has been made. The dim-countrepresents one or more premium sales to one or more customers. Somepremium sale customers have received multiple premium sales, whereasothers may have received only one premium sale. Therefore, separatingthe individual dim-count into its individual customer elements resultsin a premium sale count for each customer.

Leaping from a dim-count to individual elements that make up thedim-count may require having access to the individual elements. In thepresent embodiments, such access may be provided efficiently and withlow computational overhead through an ad-hoc query of the columnar dataset using the Spectre query optimization and machine code executioncapabilities.

Referring to FIG. 89, a guided page of responsible sales persons isshown. Various dim counts for dimension, such as corporate chain, TDtrade channel, TD sub channel, Corporate chain priority code, customerand the like are presented in this guided page. Note that for salesrepresentative Amanda, the total corporate chain dim-count is 4 and thetotal customer dim-count is 63.

Referring to FIG. 90, a multi-column/dim-count breakout function isactivated for the corporate chain dim-count.

Referring to FIG. 91, a resulting guided page is produced that breaksout the corporate chain dim-count to show 4 separate rows for salesrepresentative Amanda, one for each of the four dim-counts from thereferring guided page of FIG. 89. Further note that the total customercount of 63 is properly allocated over the four individual corporatechains.

Subsets

A computing architecture comprising a plurality of client devices and atleast one server device that facilitates presenting processed portionsof a multi-domain data set in a client electronic display may presentrequested portions through a multi-domain data set filtering process orby accessing a preconfigured duplicate of the requested portion of themulti-domain data set. The following disclosure describes techniques forautomatically operating in one or both approaches based on a data setsynchronization status between one of the plurality of client devicesand the at least one server.

In response to a user of a client electronic display device selecting apoint of entry, such as a business workflow-specific dimension indicatorthat may be disposed as a column heading in the electronic display orsuch as a data domain indicator that may be disposed as a row heading inthe electronic display a guided page processing engine be adapted toflexibly present any subset of the multi-domain data set. As noted aboveone approach is to perform filtering of the multi-domain data set toretrieve the desired portion/subset. Another approach is to access astand-alone data set that comprises a predetermined portion (e.g., adomain specific subset) of the multi-domain data set.

An active data filtering technique to limit the data that is presentedin the domain-specific subset of the multi-domain data set can beactivated through merely selecting a domain indicator (e.g. a rowheading) in a presented guided page. Once a standalone data subset isavailable for the selected domain indicator, the system mayautomatically determine whether to use an active data filteringtechnique or to directly access the standalone data subset in responseto a user selecting a domain indicator for an available standalone datasubset. Alternatively, a user my explicitly indicate that the standalonedata subset should be accessed, such as by selecting an iconrepresenting the desired domain.

Techniques for determining when and how to generate and access astandalone subset of a multi-domain data set can be embodied in avariety of inventive forms. One such form may be embodied as a method ofsynchronizing a client with a server to convert a multi-domain data setfiltering technique on the client to a data sub-set access technique onthe client after synchronization. A synchronization parameter may beconfigured to identify if a user has indicated that a subset that ispresented on a guided page should be configured as a separate datasubset during synchronization. The data subset synchronization processmay involve a user assigning a subset designation to any guidednavigation page that is being presented in the electronic clientinterface. The dimension, domain, and other narrowing parameters thatare applied to generate the guided navigation page may be captured andapplied during an upcoming synchronization of the client with a server.Once a user has designated a guided navigation page content to besynchronized as a subset, until the synchronization is performed anytime a user selects the same subset (e.g., by selecting a subset iconthat is created when the user designates a guided page to be saved as adata subset), the resulting page will be generated by applying a filterto the multi-domain data set available on the client that filters themulti-domain data set based on the dimension, domain, and any otherparameters associated with the designated subset.

Another technique related to using synchronization to generate a datasubset may facilitate presenting domain-specific data from one of afirst data set comprising data associated with a plurality of datadomains (e.g., comparable to the multi-domain data set describedelsewhere herein) and a second data set that is specific to thepresented domain. The presenting using the first or second data sets maybe in response to a user indication of the presented domain, and may bebased on a sync status of the first data set. As noted above, until themulti-domain data set that includes all of the data needed to generatethe separate sub-data set has been synchronized, each time a userselects an icon representing the indicated subset, a filtering approachis applied. However, once the first data set is synchronized after auser indicates a subset, a guided page that represents the indicatedsubset can be generated by filtering the synchronized multi-domain dataset or by directly accessing the stand-alone data set that is generatedduring the synchronization process.

A guided navigation page engine may optionally, after a synchronizationevent that produces a stand-alone data subset, maintain informationabout each stand-alone data subset so that whenever a request for datacontained in the subset is made by the user (e.g., the user makes asequence of data selections that are identical to the selections made toproduce the guided page from which the subset was initially indicated),the stand-alone data subset may be used instead of the multi-domain dataset.

Generating a stand-along data set that contains only the portion of alarger multi-dimensional data set during synchronization is automated inthe synchronization process. One approach for this automated action isthe synchronization process produces a filter-like request to theserver-based data processing engine and captures the results in astand-alone data set. Another approach is the synchronization parametersfor the subset are applied during synchronization as a request for aclient-ready data set that contains only the specified subset (e.g., thedomain specific subset).

Another embodiment of synchronization-enabled subset processing mayinclude presenting parameter-specific data as either a filtered outputof a parameter-agnostic data set or as an unfiltered output of aparameter-specific data set based on an indication of a user request fora subset and a status of synchronizing the parameter-agnostic data setwith a server. Here, the parameter-agnostic data set may be comparableto the multi-domain data set described elsewhere herein. Alternatively,the parameter-agnostic data set may itself be a subset of a potentiallylarger multi-domain data set. A sequence of user selections in presentedguided navigation pages may be aggregated (and optionally furtherprocessed) to form a subset parameter that can then be applied duringsynchronization as described elsewhere herein.

Another embodiment of synchronization-enabled subset processing mayinclude a method of generating a plurality of data sets (e.g., by aserver in response to a client synchronization request that includes atleast one subset request). The plurality of data sets may include amulti-domain data set and at least one domain-specific subset of themulti-domain data set. Generating the plurality of data sets coordinatedwith guided navigation page data set synchronization between a clientand a server may be in response to a synchronization request from aclient device, wherein the synchronization request indicates the atleast one domain. Synchronization and/or a server-based data processingengine may perceive the at least one indicated domain as a stand-alonedata subset request.

Yet another embodiment of automated processing of subset requeststhrough synchronization may include limiting client device-specific useraccess to domain-specific data from a multi-domain data set either byconfiguring a domain-specific filter for the multi-domain data set or byaccessing a domain-specific data set; the choice of which approach totake being based on a synchronization status of the client-specificdevice. As noted above, an architecture of computing devices may includea plurality of client devices (e.g., tablets, surfaces, lap tops, mobilephones, and the like) that may, from time to time, synchronize a guidednavigation page data set between the server and the plurality of clientdevices individually. Each synchronization action may includeclient-side parameters that impact the generation of domain-specificdata sets that are then synchronized with the client device. Therefore,a user may have access two otherwise identical client devices, bothbeing synchronized to the same level of synchronization. However, if oneof the devices was synchronized with a domain-specific/datasubset-specific subset generation parameter and the other wassynchronized without this parameter, only one of the two devices wouldhave a domain-specific data set synchronized to it.

A technique of multi-domain data set filtering noted in the embodimentsabove may simply temporarily limit access to a filtered subset of thelarger data set when producing a domain-specific guided page. Because afiltering approach allows for a guided page engine to access the entiremulti-domain data set (including data set content that may not beuploaded to the client device), a user may navigate outside of a domainthat he has specified through data selections that narrow a presentedpage to a specific domain by selecting a point of entry, menu items, andthe like that breaks the domain-specific nature. In this way, a user isnot limited in any way to view only data within the domain that he hasspecified.

Accessing the same domain-specific data via a domain-specific datasubset avoids the user inadvertently accessing data outside of aspecific domain simply by only having available data of the specificdomain. Further accesses and movement through interactions withselectable elements in the guided navigation pages being presented tothe user are necessarily limited to the data available in thedomain-specific data subset.

Referring to FIG. 92, an exemplary architecture is depicted that maysupport the subset-related embodiments described herein. A user query9202 may be provided, such as by a user selecting a series of activeelements (e.g., domains, dimensions, points of entry, menu items, datavalues, and the like) on one or more sequentially presented guidednavigation pages. The user query 9202 may be processed by a subsetstatus facility 9204 to determine if a subset that maybe indicated inthe query (e.g., the user may explicitly select an icon representing asubset) is accessible as a separate data subset. The query may beprocessed through to the multi-domain data set 9208 and/or may befurther processed by a subset assignment facility 1506 to determine ifthe user has requested that a subset be assigned to the query.

If a subset has been assigned to the query, but the subset has not yetbeen created, a subset synchronization parameter is configured and usedby a synchronization facility 9210 at a subsequent synchronizationaction to generate a stand-alone data subset 9212 for the assignedsubset. Although synchronization is performed between a client and aserver, because a data subset is a logical copy of (although may begenerated distinctly from) the multi-domain data set 9208, thesynchronization facility 9210 is logically depicted between themulti-domain data set 9208 and the data subset 9212. Synchronizationbetween a client and a server is more thoroughly described elsewhereherein and this logical depiction can co-exist therewith in embodiments.

If a data subset 9212 is available to the client device, such as bysynchronization as described herein, a page generation facility 9214 mayreceive all data for generating a subset-specific guided navigation page9216 from it. If a data subset 9212 is not available, output from filter9218 may be processed by a page generation facility 9214 to generate asubset page 9216.

In an alternate embodiment data subset 9212 may be stored in a cache ofgenerated page information on the server (e.g., accessible to theSpectre data processing engine) when the filtered view is generatedduring a data query by the client through the server. Synchronizing theclient with the server may cause this generated page information to becached on the server for use by the requesting client. Synchronizationmay also cause the same data to be stored on the client guided pageapplication cache. Therefore if a user of a client device effectivelygenerates a compatible query to the same data (e.g., through explicitrecitation of the query, or through a sequence of guided page accesseswhile the server cache is maintained, the data will be served from theserver cache rather than requiring the data to be generated with theSpectre engine again. After synchronizing the client and server, thedata can be present on the client as a separate subset file that can beaccessed by the client without dependence on a server or serverconnection.

FIGS. 93-97 depict screen shots related to embodiments for generatingand accessing data subsets. FIG. 93 depicts a guided navigation page9302 in which a user has selected a particular active element 9304“Amanda” as a responsible sales person. FIG. 94 depicts a resultingguided navigation page for Amanda as responsible sales person. The datawithin such a domain may be captured as a subset. This action isdepicted in FIG. 95 that shows a subset creation icon 9502 beingselected on the guided navigation page of FIG. 94. In response to theuser selection of the subset icon 1802, a confirmation screen ispresented to the user as depicted in FIG. 96. Upon confirmation, asubset icon that represents the subset parameters associated with theguided navigation page from which the subset was requested is generated.FIG. 97 depicts a menu of subsets that have been assigned, includingthose that have not yet synchronized and those that have beensynchronized. Accessing one of these subset icons may result in eitheraccessing the corresponding data subset that has been synchronized orsending a request to the server to provide the data identified by thesubset parameters as a filtered output of a server-based version of themulti-domain data set.

A method of data subset usage in a guided navigation page clientenvironment may include, storing in a processor accessible memory atleast one user selection of an active guided page generation elementpresented in a first guided navigation page that is presented on aclient device electronic display; presenting a subset creation icon on asecond guided navigation page that results from the user selection of anactive guided navigation page generation element; receiving aconfirmation from a user to generate a subset based on the second guidednavigation page and the stored at least on user selection; generating anactive subset use element and adding the active element to a subsetselection guided navigation page; in response to receiving a userselection of the active subset use element performing one of accessing astand-alone data set that comprises data associated with a data domainof the second guided navigation page and accessing data representativeof the second guided navigation page from a server by sending a requestfor the data over a network; generating a version of the second guidednavigation page based on the accessed data; and presenting the generatedsecond guided navigation page in the client device electronic display.Wherein accessing data representative of the second guided navigationpage from the server comprises checking if the stand-alone data set isaccessible to the processor of the client device. Wherein accessing datarepresentative of the second guided navigation page from the servercomprises checking if a data that corresponds to the data representativeof the second guided navigation page or the second guided navigationpage is accessible by a processor of the client device in aclient-device processor accessible memory.

Filter/Multi-View

In the methods and systems of guide page generation and operation on atablet-based device and the like, filtering of data may be performed tofacilitate presenting domain or dimension-specific views of the columnardata that is built by the Spectre engine, also as described herein. Onesuch embodiment of presenting domain-specific and/or dimension-specificviews comprises making both a filtered and unfiltered view available ina guided page. This embodiment may effect a multiple view capability ofa data source in a single guided page and therefore is referred hereintoas multi-view pages.

In an embodiment, a guided navigation page may be generated forpresentation on a client device in which aggregated data for a pluralityof data domains of a multi-domain data set is presented in a firstdisplayable tab of the page and domain-specific data for auser-identified domain of the plurality of data domains is presented ina second displayable tab of the page, wherein the second tab representsa filtered view of the data in the first tab. Further, updates to thedata presented in the first tab (e.g., aggregated multi-domain data froma columnar data set that is generated using the Spectre engine and/orrelated capabilities such as a c-plan and the like as described herein)that impact the filtered view may result in corresponding changes to thesecond tab. Additionally, the first tab may itself represent a firstfiltered view of the columnar data. Even further, the data in the firsttab may represent data that has been filtered multiple times from asource data set. Alternatively, the data for the first tab and/or forthe second tab may be sourced from a stand-alone data subset that isgenerated through a server-client synchronization operation. Generatingand using stand-alone data subsets are described elsewhere herein.

Multi-view data in a first tab and second tab may be related to eachother in that each may represent a different view of common source data,with a second tab representing a more narrowly filtered view of thefirst tab. However, both tabs may represent multi-domain and/ormulti-dimension data so that a second tab represents multiple domainsbut is nonetheless a filtered view of data in the first tab.

Selecting active elements, and the like in each of the first and secondtabs may activate different follow-on guided pages. In an example, afirst page that represents data for a plurality of responsiblesalespersons may include an active element for each salesperson thatrepresents the total number of customers for the salesperson. Selectingthis active element may generate a resulting guided page that shows thecustomers for the specific salesperson. A filtered second tab may showdata for a plurality of responsible salespersons but filtered for aspecific geographic region. Selecting a corresponding active element inthe second tab may result in producing a guided page that lists thesuppliers represented by the data in the active element that are limitedto the specific geographic region.

Other embodiments of multi-view pages may include calculated comparisonof data in the first and second tabs. This may include a first tab beingunfiltered, a second tab being filtered, and a third tab being thecalculated comparison of the first and second tabs.

Another embodiment of a multi-tab view page may comprise more than twotabs, each showing refined filtering so that a third tab may represent afiltered view of a second tab that may represent a filtered view of afirst tab.

In yet another alternative multi-view guided page embodiment, a firsttab may represent first domain-specific data as a filtered view of aSpectre engine generated columnar data set, and a second tab of theguided page may represent second domain-specific data of the samecolumnar data set, wherein the first and second domain-specific data mayhave a common dimension. In this embodiment, a dimension specific viewof the columnar data set may be presented in two distinctdomain-specific views on separate tabs of a single guided page.

Referring to FIGS. 98 and 99, two tabs of a multi-view guided page aredepicted. FIG. 98 depicts an information tab of a specific customer fora specific sales representative. FIG. 99 depicts an analysis tab for thesame customer/sales representative.

Referring to FIGS. 100 and 101, two tabs of an alternate embodiment of amulti-view guided page are depicted. FIG. 100 depicts an overview of acorporate brand for a supplier dimension. FIG. 101 depicts a filteredview of a specific corporate supplier within the corporate brand view ofFIG. 100.

Machine Embodiments

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The processor may be part of aserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions and the like.The processor may be or include a signal processor, digital processor,embedded processor, microprocessor or any variant such as a co-processor(math co-processor, graphic co-processor, communication co-processor andthe like) and the like that may directly or indirectly facilitateexecution of program code or program instructions stored thereon. Inaddition, the processor may enable execution of multiple programs,threads, and codes. The threads may be executed simultaneously toenhance the performance of the processor and to facilitate simultaneousoperations of the application. By way of implementation, methods,program codes, program instructions and the like described herein may beimplemented in one or more thread. The thread may spawn other threadsthat may have assigned priorities associated with them; the processormay execute these threads based on priority or any other order based oninstructions provided in the program code. The processor may includememory that stores methods, codes, instructions and programs asdescribed herein and elsewhere. The processor may access a storagemedium through an interface that may store methods, codes, andinstructions as described herein and elsewhere. The storage mediumassociated with the processor for storing methods, programs, codes,program instructions or other type of instructions capable of beingexecuted by the computing or processing device may include but may notbe limited to one or more of a CD-ROM, DVD, memory, hard disk, flashdrive, RAM, ROM, cache and the like.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server and other variants such as secondaryserver, host server, distributed server and the like. The server mayinclude one or more of memories, processors, computer readable media,storage media, ports (physical and virtual), communication devices, andinterfaces capable of accessing other servers, clients, machines, anddevices through a wired or a wireless medium, and the like. The methods,programs or codes as described herein and elsewhere may be executed bythe server. In addition, other devices required for execution of methodsas described in this application may be considered as a part of theinfrastructure associated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe invention. In addition, all the devices attached to the serverthrough an interface may include at least one storage medium capable ofstoring methods, programs, code and/or instructions. A centralrepository may provide program instructions to be executed on differentdevices. In this implementation, the remote repository may act as astorage medium for program code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe invention. In addition, all the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements.

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network or code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like.

The methods, programs codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on a peer topeer network, mesh network, or other communications network. The programcode may be stored on the storage medium associated with the server andexecuted by a computing device embedded within the server. The basestation may include a computing device and a storage medium. The storagedevice may store program codes and instructions executed by thecomputing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g. USB sticks or keys),floppy disks, magnetic tape, paper tape, punch cards, standalone RAMdisks, Zip drives, removable mass storage, off-line, and the like; othercomputer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipments, servers, routers and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps thereof, may berealized in hardware, software or any combination of hardware andsoftware suitable for a particular application. The hardware may includea general purpose computer and/or dedicated computing device or specificcomputing device or particular aspect or component of a specificcomputing device. The processes may be realized in one or moremicroprocessors, microcontrollers, embedded microcontrollers,programmable digital signal processors or other programmable device,along with internal and/or external memory. The processes may also, orinstead, be embodied in an application specific integrated circuit, aprogrammable gate array, programmable array logic, or any other deviceor combination of devices that may be configured to process electronicsignals. It will further be appreciated that one or more of theprocesses may be realized as a computer executable code capable of beingexecuted on a machine readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the invention has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present invention isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

All documents referenced herein are hereby incorporated in theirentirety by reference.

We claim:
 1. A computer-implemented method comprising: accessing datafrom a plurality of disparate data sources that are used in a workflowfor a particular business activity, the plurality of disparate datasources consisting of internal data sources of the particular businessactivity and external data sources, the accessed data comprisingdifferent and distinct first and second data domains among a pluralityof distinct data domains; determining a plurality of core dimensions ofthe accessed data, at least one of the plurality of core dimensionsbeing common to the distinct first and second data domains, the at leastone of the plurality of core dimensions identifying a data type withinthe plurality of distinct data domains for enabling the workflow for theparticular business activity; generating a data set from the pluralityof disparate data sources using a columnar data generation engine thataligns data from at least a portion of each of the plurality ofdisparate data sources with the common core dimensions to produce acolumnar data structure; generating machine-specific code from a userinquiry, the code comprising a set of logical instructions for accessingthe data set based on the content of the inquiry; and deriving from thedata set with the machine-specific code a plurality of guided pages thatare presented in a user interface on an interactive electronic display,the plurality of guided pages derived to include predetermined workflowinformation for the particular business to facilitate direct navigationfrom the first domain to the second domain of the plurality of distinctdata domains, wherein the direct navigation is responsive to a singleuser interaction of an actionable element presented in the userinterface for navigating to an area of interest based on the workflowfor the particular business activity, and wherein a first guided page ofthe plurality of guided pages includes: the actionable elementrepresenting data from the distinct first data domain, wherein userselection in the user interface of the actionable element facilitatespresentation in the user interface of a follow-on guided page comprisingone of: a second guided page of the distinct first data domain; a guidedpage of the plurality of distinct data domains including at least one ofthe distinct first and second data domains; or a guided page resultingfrom direct navigation from the distinct first domain to the distinctsecond data domain, due at least in part to a core dimension from theplurality of core dimensions that is common to the distinct first andsecond data domains for the workflow of the particular businessactivity.
 2. The computer-implemented method of claim 1, whereinfacilitating direct navigation from the first domain to the seconddomain comprises producing data for the follow-on guided page from thedata set by executing machine-specific code that represents logicalinstructions for accessing the data set that are determined from theuser-selected actionable element.
 3. The computer-implemented method ofclaim 1, wherein the logical instructions for accessing the data setthat are determined from the user-selected actionable element arefurther determined from at least one of the common core dimensions andthe first data domain.
 4. The computer-implemented method of claim 1,wherein processing the data set to produce data for the follow-on guidedpage comprises stateless processing of the data set.
 5. Thecomputer-implemented method of claim 1, wherein generating a data setcomprises executing a plurality of steps selected from a list of stepsconsisting of: parsing source data, harmonizing source data formats,normalizing strings of the source data per language of a locale orindustry specific jargon, performing binary encoding and packing ofnumber values, preparing labels that act as links for attributes to dataentries, and sorting strings.
 6. The computer-implemented method ofclaim 1, wherein the machine-specific code is optimized to group queryfunctions into common processor execution threads based on usage ofcommon data sets by different functions.
 7. The computer-implementedmethod of claim 1, wherein the machine-specific code is optimized togroup common calculations across query functions.
 8. Thecomputer-implemented method of claim 1, wherein the internal datasources comprise at least two sources selected from the list consistingof flat files, spreadsheets, data warehouses, SQL databases,non-standard formatted data, legacy systems, transactional systems, andEnterprise Resource Planning (ERP) systems.
 9. The computer-implementedmethod of claim 1, wherein the external data sources comprise at leasttwo sources selected from third party feeds, market data, end-user data,state data, county data, regional data, demo data, legacy systems, ERPsystems, spreadsheets, data warehouses, SQL databases, suppliers,distributors, government entities, product information, state laws andregulations, federal laws and regulations, local laws and regulations,customer information, purchase history, news, and industry guidelines.10. A computer-implemented system comprising: a plurality of disparatedata sources that are used in a workflow for a particular businessactivity, the plurality of disparate data sources consisting of internaldata sources of the particular business activity and external datasources, the data comprising different and distinct first and seconddata domains and a plurality of core dimensions, at least one of theplurality of core dimensions being a common core dimension to at leasttwo of the distinct data domains, the at least one of the plurality ofcore dimensions identifying a data type within the distinct data domainsfor enabling the workflow for the particular business activity; acolumnar data set generation engine for generating a columnar datastructure from the plurality of disparate data sources, wherein thecolumnar data structure is generated so that at least a portion of theplurality of disparate data sources are aligned with the plurality ofcore dimensions; machine-specific code that is derived from a userinquiry, the code comprising a set of logical instructions for accessingthe data structure based on the content of the inquiry; and a pluralityof guided pages adapted for being presented in a user interface on aninteractive electronic display, the plurality of guided pages includingpredetermined workflow information for the particular business tofacilitate direct navigation from the distinct first domain to thedistinct second domain, wherein the direct navigation is responsive to asingle user interaction of an actionable element for navigating to anarea of interest based on the workflow for the particular businessactivity, and wherein at least a portion of the plurality of guidedpages comprise data that is generated by processing the columnar datastructure based on the user inquiry, and further wherein a first guidedpage of a plurality of guided pages includes: the actionable elementrepresenting data from at least the distinct first domain, wherein userselection in the user interface of the actionable element facilitatespresentation in the user interface of a follow-on guided page comprisingone of: a second guided page of the distinct first data domain; a guidedpage of the plurality of distinct data domains including at least one ofthe distinct first data domain and the distinct second data domain; or aguided page of the distinct second data domain that is associated with acore dimension that is common to the distinct first and second datadomains for the workflow of the particular business activity.
 11. Thecomputer-implemented system of claim 10, wherein the facilitating directnavigation from the distinct first domain to the distinct second domaincomprises producing data for the follow-on guided page bymachine-specific code that represents logical instructions for accessingthe data set that are determined from the user-selected actionableelement.
 12. The computer-implemented system of claim 11, wherein thelogical instructions for accessing the data set that are determined fromthe user-selected actionable element are further determined from atleast one of the common core dimensions and the first data domain. 13.The computer-implemented system of claim 11, wherein data for thefollow-on guided page is produced via stateless processing of the dataset.
 14. The computer-implemented system of claim 11, wherein themachine-specific code is optimized to group query functions into commonprocessor execution threads based on usage of common data sets bydifferent functions.
 15. The computer-implemented system of claim 11,wherein the machine-specific code is optimized to group commoncalculations across query functions.
 16. The computer-implemented systemof claim 11, wherein the data set facilitates native machine caching ofsimilar data by configuring similar data to be physically proximal in anon-transient computer memory, thereby improving computer performance.17. The computer-implemented system of claim 11, wherein themachine-specific code disposes of all query-specific state information.18. The computer-implemented system of claim 11, wherein themachine-specific code retains in memory a query result table and acorresponding portion of the data set upon completion of executing themachine-specific code.
 19. The computer-implemented system of claim 11,wherein the internal data sources comprise at least two sources selectedfrom the list consisting of flat files, spreadsheets, data warehouses,SQL databases, non-standard formatted data, legacy systems,transactional systems, and Enterprise Resource Planning (ERP) systems.20. The computer-implemented system of claim 11, wherein the externaldata sources comprise at least two sources selected from third partyfeeds, market data, end-user data, state data, county data, regionaldata, demo data, legacy systems, ERP systems, spreadsheets, datawarehouses, SQL databases, suppliers, distributors, government entities,product information, state laws and regulations, federal laws andregulations, local laws and regulations, customer information, purchasehistory, news, and industry guidelines.